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With the huge demand for graph analytics in many real-world applications, massive iterative graph processing jobs are concurrently performed on the same graphs and suffer from significant high data access cost. To lower the data access cost toward high performance, several out-of-core concurrent graph processing solutions are recently designed to handle concurrent jobs by enabling these jobs to share the accesses of the same graph data. However, the set of active vertices in each partition are usually different for various concurrent jobs and also evolve with time, where some high-degree ones (or called hub-vertices) of these active vertices require more iterations to converge due to the power-law property of real-world graphs. In consequence, existing solutions still suffer from much unnecessary I/O traffic, because they have to entirely load each partition into the memory for concurrent jobs even if most vertices in this partition are inactive and may be shared by a few jobs. In this paper, we propose an efficient structure-aware storage system, called GraphSO, for higher throughput of the execution of concurrent graph processing jobs. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. The key design of GraphSO is a fine-grained storage management scheme. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions (i.e., each logical partition is a subset of active chunks) for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5 times, 2.1-4.3 times, 1.9-4.1 times, 1.9-2.9 times, 1.5-3.1 times, 1.3-1.5 times, and 1.3-2.7 times after integrating with them, respectively. [ABSTRACT FROM AUTHOR]

In recent years, targeted therapy and immunotherapy have become ideal choices for the treatment of advanced, metastatic, recurrent, and drug-resistant nasopharyngeal carcinoma (NPC), but the lack of understanding of the relationship and mechanism between TILs and angiogenic factors hinders therapeutic development and optimization. In this study, the expression of angiogenesis-related markers (VEGF-A,VEGFR-2) and TILs (CD4 + T,CD8 + T) was studied by using immunohistochemistry (IHC). Then we constructed an immunohistochemical scoring model for the co-expression of angiogenesis-related markers and TILs (CO V+TIL score)in the training (n = 124) and validated the accuracy and reliability of the scoring system in the validation cohorts (n = 114), respectively We established the CO V+TIL score model and stratified patients into different risk level in the training cohorts according to CO V+TIL score (cut-off value=28). Patients in the high-risk group had worse prognosis in the training cohorts five-year overall survival (OS), progression-free survival (PFS), locoregional relapse-free survival (LRRFS), and distant metastasis-free survival (DMFS) was lower than that of patients in the low-risk group, and this result was validated in the validation cohorts (5-year OS in the high-risk and the low-risk group 46.8% vs. 83.4%, HR: 3.42, 95%CI: 1.77–6.61, p < 0.001); (5-year PFS 45.9% vs. 81.2%, HR: 3.22, 95%CI: 1.71–6.06, p < 0.001); (5-year LRRFS 74.6% vs. 87.5%, HR: 3.22, 95%CI: 1.16–8.93, p = 0.027); and (5-year DMFS79.2% vs. 93.2%, HR: 2.22, 95%CI: 0.91–5.39, p = 0.086). Upon multivariable analysis, CO V+TIL score emerged as an independent prognostic indicator for defining survival in the training cohorts and the validation cohorts. Combining the CO V+TIL score and TNM stage improved the prediction ability of the survival. In conclusion, NPC patients with high CO V+TIL score showed worse prognosis. [ABSTRACT FROM AUTHOR]

Time-evolving stream datasets exist ubiquitously in many real-world applications where their inherent hot keys often evolve over times. Nevertheless, few existing solutions can provide efficient load balancing on these time-evolving datasets while preserving low memory overhead. In this paper, we present a novel load balancing mechanism (named FISH), which can provide the efficient time-evolving stream processing at scale through recent hot keys identification and worker assignment. The key insight of this work is that the keys of time-evolving stream data can have a skewed distribution within the bounded distance of time interval. This enables to accurately identify the recent hot keys for the real-time load balancing within a bounded scope. We therefore propose an epoch-based recent hot key identification with specialized intra-epoch frequency counting (for maintaining low memory overhead) and inter-epoch hotness decaying (for suppressing superfluous computation). We also propose to heuristically infer the accurate information of remote workers through computation rather than communication for cost-efficient worker assignment. We have integrated our approach into Apache Storm. Our results on a cluster of 128 nodes for both synthetic and real-world stream datasets show that FISH significantly outperforms state-of-the-arts with the average and the 99th percentile latency reduction by 87.12 and 76.34 percent (versus W-Choices), and memory overhead reduction by 96.66 percent (versus Shuffle Grouping). [ABSTRACT FROM AUTHOR]

In Infrastructure-as-a-Service clouds, virtual machine provisioning time is an important metric for evaluating the quality of service (QoS) provided by the service providers. VM provisioning is usually time-consuming, especially when many VMs boot up simultaneously. We call such phenomenons "Boot Storm". In practice, many VMs would run on a single compute node for higher resource utilization. This multiple VMs and single compute node scenario easily encounters Boot Storm. Boot Storm leads to extremely high latency of VM provisioning and badly affects user's experience. In this paper, we propose VMBKS, which is a cloud shared memory cache system based on booting kernel. We exploit VMs' correlations to organize VM image files, to reduce cache size and mitigate the effect of Boot Storm. We use booting kernel to construct cache and share the booting kernel to correlative VMs. Evaluation shows that VMBKS can speed up VM provisioning time by up to 60 % and mitigate the effect of Boot Storm significantly. [ABSTRACT FROM AUTHOR]

Most of dynamic data race detection essentially relies on the underlying happens-before orders to yield the precise reports. They are notoriously prone to a prohibitively basic overhead. Although there exist a wealth of research advances that succeed in significantly reducing the analysis overhead on memory accesses, there remains an open problem in handling a great deal of fundamentally unscalable synchronization overhead, which can be particularly serious for the large, lock-intensive programs with a long running time and a large number of threads. In this paper, we revisit the synchronization problem of off-the-shelf race detection with a comprehensive study. The key insight of this work is that a full collection of partial orders for synchronization operations in prior work is not necessarily tracked and analyzed from a new perspective of “global clock” representation. We therefore develop this insight into a novel pending-period based approach, aiming at reducing the overhead of monitoring and analysis on unnecessary synchronization operations. Further, we also enable a significant improvement for enhancing the efficiency of existing sampling techniques, in which synchronization operations are often conservatively identified. Our experimental results on a wide variety of programs show that our approach outperforms state-of-the-art by 5.85x (versus FastTrack), 3.51x (versus ThreadSanitizer) and 1.34x (versus IFRit) program execution slowdown improvement on average, which can be more significant as the number of threads is increasing. Particularly for the lock-intensive programs (e.g., barnes), our approach can be 26.04x faster than FastTrack. Further, our pending period extended sampling is more efficient than Pacer (with up to 31.28 percent improvement in the case of 10 percent sampling rate). [ABSTRACT FROM AUTHOR]

CRISPR-Cas12a system opens up a new avenue for biosensing due to its flexible programmability and excellent target recognition accuracy. In this work, a novel label-free fluorescent biosensor for highly sensitive detection of bleomycin (BLM) was developed based on BLM-mediated inactivation of CRISPR-Cas12a and thioflavin T (ThT)-induced G-quadruplex formation. We designed a single-stranded DNA (ssDNA) probe containing the scission site of BLM as a DNA activator for Cas12a-crRNA. In the absence of BLM, the DNA activator binds to and activates the Cas12a, the activated CRISPR-Cas12a system trans-cleaves the ThT-induced G-quadruplex formation. Upon the addition of BLM, the DNA activator undergoes an irreversible cleavage event at the scission site via the oxidative effect of BLM with Fe(Ⅱ) as a cofactor. As a result, the trans-cleavage activity of Cas12a cannot be activated so the G-quadruplex-ThT complex remains intact, resulting in a significant increase of fluorescence signal. Attributing to the specific recognition of BLM and the excellent cleavage activity of Cas12a on DNA G-quadruplexes, the fluorescent biosensor exhibits high sensitivity and selectivity for detection of BLM in human serum samples, illustrating a promising tool for BLM assay in biomedical research. [Display omitted] • A novel label-free fluorescent strategy is developed based on CRISPR-Cas12a and G-quadruplex-ThT complex. • The fluorescent sensing strategy exhibits high sensitivity for BLM detection with a detection limit down to 45 pM. • The proposed strategy can be applied for the determination of BLM in human serum samples. • This work broadens the applications of CRISPR/Cas-based sensing systems in biomedical research. [ABSTRACT FROM AUTHOR]

Graph algorithm is pervasive in many applications ranging from targeted advertising to natural language processing. Recently, Asynchronous Graph Processing (AGP) is becoming a promising model to support graph algorithm on large-scale distributed computing platforms because it enables faster convergence speed and lower synchronization cost than the synchronous model for no barrier between iterations. However, existing AGP methods still suffer from poor performance for inefficient vertex state propagation. In this paper, we propose an effective and low-cost forward and backward sweeping execution method to accelerate state propagation for AGP, based on a key observation that states in AGP can be propagated between vertices much faster when the vertices are processed sequentially along the graph path within each round. Through dividing graph into paths and asynchronously processing vertices on each path in an alternative forward and backward way according to their order on this path, vertex states in our approach can be quickly propagated to other vertices and converge in a faster way with only little additional overhead. In order to efficiently support it over distributed platforms, we also propose a scheme to reduce the communication overhead along with a static priority ordering scheme to further improve the convergence speed. Experimental results on a cluster with 1,024 cores show that our approach achieves excellent scalability for large-scale graph algorithms and the overall execution time is reduced by at least 39.8 percent, in comparison with the most cutting-edge methods. [ABSTRACT FROM PUBLISHER]

Recently, there have been many disk-based systems proposed for iterative graph processing. In the popular vertex/edge-centric systems, an iterative directed graph algorithm needs to reprocess many partitions so as to update their vertices’ states according to other non-convergent vertices for the unawareness of their dependencies. As a result, it induces high data access cost and a long time to converge. To tackle this problem, we propose a novel system for iterative directed graph processing with taking advantage of the strongly connected component (SCC) structure. It stores a directed graph into a directed acyclic graph (DAG) sketch, with each node representing a SCC in the original data graph. During execution, the SCCs are loaded into memory for processing in a parallel way according to the topological order of the DAG sketch, and the vertices in each SCC are tried to be handled along the directed paths. In this way, each SCC is able to reach convergence in order and needs to be loaded into the main memory for exactly once, getting much lower data access cost and faster convergence. Besides, the vertices of each SCC need fewer updates for convergence. We further develop a lightweight approach to maintain the DAG sketch and handle SCCs in an incremental way for evolving graphs. Compared with the state-of-the-art methods, experimental results show that our approach achieves a performance improvements of 1.46-8.37 times for static graphs, and can reduce the execution time by 61.4-72.7 percent for evolving graphs. [ABSTRACT FROM AUTHOR]

Cloud gaming is a promising approach to provide high-quality gaming services to mobile devices. However, existing cloud gaming systems fail to fully exploit hardware resources due to coarse-grained resource scheduling based on virtual machine migration. In this article, we propose a novel cloud gaming design, referred to as FGCG, with fine-grained scheduling to maximize resource utilization on a heterogeneous CPU-GPU cluster. Specifically, we decompose game workloads into small and independent render tasks that can be freely dispatched to different machines. Trace-driven simulation results show that FGCG can significantly improve resource utilization compared to existing cloud gaming systems. [ABSTRACT FROM AUTHOR]

The critical sections with the lock protection greatly limit the concurrency of multi-threaded applications. The prior lock elision based technique is presented to exploit the parallelism between critical sections accessing the disjoint shared data, but still fails to notice and expose a high degree of concurrency between critical sections that contend for the same shared data, i.e., conflicting critical sections (CCS). This paper focuses on exploiting the CCS parallelism. The key insight of this work is that, for each running CCS, a large proportion ( $>$ 73.4%) of parallelism between CCSs can be exploited as fully as possible by simply allowing the parallel execution of their first conflict-free code fragment at runtime. We therefore present BSOptimizer, a new microarchitecture, to perform the partial reversion integrated with a series of sophisticated hardware and software strategies for the CCS parallelization. We complement the off-the-shelf cache coherency protocol to perceive the conflict location of CCS, present a predictive checkpoint mechanism to register and predict the concerned conflict point in a lightweight and accurate fashion, and redefine the traditional mutual exclusive semantics with a binary relationship. With these collaborative techniques, each CCS can be scheduled in parallel. Our experimental results on a wide variety of real programs and PARSEC benchmarks show that, compared to the native execution and two state-of-the-art lock elision techniques (including SLE and SLR), BSOptmizer can dramatically improves the performance of programs with a slight ($<$ 0.8%) energy consumption and ($<$ 3.9%) extra runtime overhead. Our evaluation on a micro-benchmark with software based optimization also verifies that BSOptimizer can accurately exploit the CCS parallelism as promised. [ABSTRACT FROM AUTHOR]

For large-scale graph analysis on a single PC, asynchronous processing methods are known to converge more quickly than the synchronous approach, because of more efficient propagation of vertices state. However, current asynchronous methods are still very suboptimal in propagating state across different graph partitions. This presents a bottleneck for cross-partition state update and slows down the convergence of the processing task. To tackle this problem, we propose a new method, named the HotGraph, to faster graph processing by extracting a backbone structure, called hot graph, that spans all the partitions of the original graph. With this approach, most cross-partition state propagations in traditional solutions now take place within only a few hot graph partitions, thus removing the cross-partition bottleneck. We also develop a partition scheduling algorithm to maximize the hot graph’s effectiveness by keeping it in memory and assigning it the highest priority for processing as much as possible. A forward and backward sweeping execution strategy is then proposed to further accelerate the convergence. Experimental results show that HotGraph can reduce the number of vertex state updates processed by 51.5 percent, compared with state-of-the-art schemes. Applying our optimizations further reduces this number by 72.6 percent and the execution time by 80.8 percent. [ABSTRACT FROM AUTHOR]

Parameter-server frameworks play an important role in scaling-up distributed deep learning algorithms. However, the constant growth of neural network size has led to a serious bottleneck on exchanging parameters across machines. Recent efforts rely on manually setting a parameter-exchanging interval to reduce communication overhead, regardless of the parameter-server's resource availability as well. It may face poor performance or inaccurate results for inappropriate interval. Meanwhile, request burst may occur, exacerbating the bottleneck. In this paper, we propose an approach to automatically set the optimal exchanging interval, aiming to remove the parameter-exchanging bottleneck and to evenly utilize resources without losing training accuracy. The key idea is to increase the interval on different training nodes on the basis of the knowledge of available resources and choose different intervals for each slave node to avoid request bursts. We adopted this method to optimize the parallel Stochastic Gradient Descent algorithm, through which we successfully sped up parameter-exchanging process by eight times. [ABSTRACT FROM AUTHOR]

Cache pollution, by which weak-locality data unduly replaces strong-locality data, may notably degrade application performance in a shared-cache multicore machine. This paper presents NightWatch, a cache management subsystem that provides general, transparent and low-overhead pollution control to applications. NightWatch is based on the observation that data within the same memory chunk or chunks within the same allocation context often share similar locality property. NightWatch embodies this observation by online monitoring current cache locality to predict future behavior and restricting potential cache polluters proactively. We have integrated NightWatch into two popular allocators, tcmalloc and ptmalloc2. Experiments with SPEC CPU2006 show that NightWatch improves application performance by up to 45 percent (18 percent on average), with an average monitoring overhead of 0.57 percent (up to 3.02 percent). [ABSTRACT FROM PUBLISHER]

The new emergence of data-intensive applications raises a huge requirement on the capacity of memory. However, an obvious increase of memory makes the corresponding energy consumption unacceptable in practice. To address this question, any effort only to reduce the energy consumption of dynamic random access memory (DRAM) is ineffective. Recently, combining DRAM and phase change memory (PCM) to construct a hybrid main memory is recognized as a promising solution to distinctly reduce the energy consumption. In this paper, we propose a new page-level energy management strategy to optimize the energy consumption of the hybrid main memory. The proposed strategy records pages' local and global access information by a new data structure, and then classifies pages by the access history, at last adaptively places PCM or DRAM pages according to the memory characteristics and remaps the migrated pages. Our experimental results show that our strategy can achieve 9.4 % of energy saving and 9.6 % of performance improvement at most compared with APG and PDRAM, which were proposed respectively by conferences RACS'12 and DAC'09. [ABSTRACT FROM AUTHOR]

Locks have been widely used as an effective synchronization mechanism among processes and threads. However, we observe that, a large number of false inter-thread dependencies (i.e., unnecessary lock contentions) exist during the program execution on multicore processors, incurring significant performance overhead. This paper presents a performance debugging framework, PERFPLAY, to facilitate the identification of unnecessary lock contentions and to guide programmers to improve the program performance by eliminating the unnecessary lock contentions. Since the performance debugging of unnecessary lock contentions is input-sensitive, we first identify the representative inputs for performance debugging. Next, PERFPLAY quantifies the performance impact of unnecessary lock contention code regions for each candidate input. Taking into account conflicting attribute of performance impact and input coverage in the real world, we finally make the tradeoff between performance impact and input coverage to recommend the optimal unnecessary lock contention code regions. Our final results on five real-world programs and PARSEC benchmarks demonstrate the significant performance overhead of unnecessary lock contentions, and the effectiveness of PERFPLAY in troubleshooting the target unnecessary lock contention code regions with the consideration of both performance impact and input coverage. [ABSTRACT FROM AUTHOR]

In multi-program environment, cache contention among processors can significantly degrade system performance. Cache partitioning served as an effective measure has been widely studied, especially for dynamic cache partitioning. However, it is difficult to decide the best cache quota which should be allocated to co-scheduled programs and the best time when a cache adjusting should be performed in dynamic cache partitioning scheme. This paper presents a novel dynamic cache partitioning mechanism based on the phase behavior of programs. It uses the performance monitoring units of modern processors and detects the phase behavior of programs to guide the cache partitioning at run-time. Since programs have recurring phase behavior during the whole execution time, on one hand, we can adjust the cache quota when a phase change occurs, on the other hand, we can make cache partitioning policy with higher accuracy and lower overhead by classifying phases. The method proposed in this work is validated in the measured results for applications from SPEC CPU 2006 benchmark suite. Compared with the performance of shared cache scheme, our method can achieve a speedup up to 1.214 for co-scheduled applications. [ABSTRACT FROM AUTHOR]

The and of OH radical with A·U base pair have been explored by using density functional theory (DFT) both in gas phase and in aqueous solution. Solvent effects were taken into consideration by using the polarized continuum model. All the reaction pathways are exothermic in energy, and the compounds in aqueous phase are more favorable than those in gas phase. The relative free energies of adducts in the are lower than those obtained for products in reaction. Among reaction, the hydrogen abstractions from AC2·U and AN6·U sites are more favorable than those from AC8·U, A·UC5, and A·UC6 sites. In addition, at AC8·U, A·UC5, and A·UC6 sites are more probable than other investigated positions. The at AH8·U site is most favorable, and at A·UC5 site is more preference than that at A·UC6 site controlled by the kinetics factors. The data in both gas phase and water solution demonstrated that addition of OH radical to A·UC5 and A·UC6 sites are more thermodynamically and kinetically favorable than abstracting the hydrogen atom form A·UC5 and A·UC6 sites. The phenomena are in agreement with the experimental observations. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Large-scale behavioral simulations are widely used to study real-world multi-agent systems. Such programs normally run in discrete time-steps or ticks, with simulated space decomposed into domains that are distributed over a set of workers to achieve parallelism. A distinguishing feature of behavioral simulations is their frequent and high-volume group migration, the phenomenon in which simulated objects traverse domains in groups at massive scale in each tick. This results in continual and significant load imbalance among domains. To tackle this problem, traditional load balancing approaches either require excessive load re-profiling and redistribution, which lead to high computation/communication costs, or perform poorly because their statically partitioned data domains cannot reflect load changes brought by group migration. In this paper, we propose an effective and low-cost load balancing scheme, named Inc-part, based on a key observation that an object is unlikely to move a long distance (across many domains) within a single tick. This localized mobility property allows one to efficiently estimate the load of a dynamic domain incrementally, based on merely the load changes occurring in its neighborhood. The domains experiencing significant load changes are then partitioned or merged, and redistributed to redress load imbalance among the workers. Experiments on a 64-node (1,024-core) platform show that Inc-part can attain excellent load balance with dramatically lowered costs compared to state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Energy consumption is now a growing problem and leads to high costs in large data center based on virtualization technology. In data center, large memory consumes the main part of total energy, up to 41%. Observed from our experiments, we find that the dispersion characteristic of the virtual machine memory has a great impact on the overall energy consumption. This paper redesigns virtual machine's memory allocation scheme to reduce memory's energy consumption in virtual machine monitor. Two heuristic algorithms of allocating memory for virtual machines are proposed. Besides, based on the new memory allocations, we also design a dynamic memory management system for runtime environment. Experimental results show that our scheme achieves above 50% power reduction. [ABSTRACT FROM AUTHOR]

In this report, a new ligand TFBIP (TFBIP = 2-(4′-trifluoromethyl)-[1,1′-biphenyl]-4-yl)-1 H -imidazo[4,5-f][1,10]phenanthroline) and its three iridium (III) complexes [Ir(ppy) 2 (TFBIP)](PF 6) (Ir1 , ppy = 2-phenylpyridine), [Ir(bzq) 2 (TFBIP)](PF 6) (Ir2 , bzq = benzo[ h ]quinolone) and [Ir(piq) 2 (TFBIP)](PF 6) (Ir3 , piq = 1-phenylisoquinoline) were synthesized and characterized. The cytotoxicity in vitro of the complexes toward several cancer cells was evaluated by 3-(4,5-dimethylthiazole-2-yl)-2,5-biphenyl tetrazolium bromide (MTT) methods. The complexes show no cytotoxicity (IC 50 > 100 μM) against these cancer cells. To enhance anticancer activity, these complexes were trapped in liposomes to form Ir1Lipo , Ir2Lipo and Ir3Lipo. The liposomes Ir1Lipo , Ir2Lipo and Ir3Lipo exhibit high or moderate cytotoxic activity. In particular, Ir1Lipo can effectively inhibit the cell growth with a low IC 50 value (< 10 μM) toward A549, HepG2, BEL-7402, B16, HeLa and SGC-7901 cells. Surprisingly, Ir1Lipo has no cytotoxic activity against the normal cell LO2 (IC 50 > 100 μM). The apoptosis and pyroptosis were investigated. Ir3Lipo induces apoptosis with a high early apoptotic number of 37%. The reactive oxygen species (ROS) levels, mitochondrial permeability transition pore open and mitochondrial membrane potential were detected. The intracellular Ca2+ concentration and release of cytochrome c were investigated. The expression of Bcl-2 (B-cell lymphoma-2) family proteins was explored by western blot. The antitumor activity in vivo of Ir1Lipo was evaluated with an inhibitory rate of 53%. Iridium(III) complexes were synthesized and characterized. These complexes display no cytotoxic activity. However, the complexes were encapsulated into the liposomes to form Ir1Lipo , Ir2Lipo and Ir3Lipo , Ir1Lipo show high anticancer efficacy toward the selected cancer cells. The anticancer effect in vivo of Ir1Lipo was investigated. [Display omitted] • Three iridium(III) complexes and their liposomes were synthesized and characterized. • The cytotoxicity in vitro and in vivo of the complexes and liposomes was studied. • The apoptosis, pyroptosis and cell cycle arrest were performed. • Reactive oxygen species levels and mitochondrial membrane potential were investigated. • The expression of Bcl-2 family proteins was assayed by western blot analysis. [ABSTRACT FROM AUTHOR]

Abstract: Owing to the isolation barrier between VMs, inter-domain communication suffers great performance loss. Current solutions widely exploit inter-domain shared memory mechanism to improve performance. Also the larger the shared memory buffer is, the higher the throughput and the less the latency. However, these solutions which use static fixed size shared memory, do not take memory utilization and the heterogeneous upper applications into consideration. In this paper, we have designed and implemented an adaptive shared memory mechanism for inter-domain communication, called AdaptIDC, adjusting the shared memory dynamically. With the help of completely independent in/out buffer design, the IOIHMD adjustment algorithm, the control ring and event channel reuse mechanism, AdaptIDC achieves superior shared memory utilization and yet does not sacrifice high performance between co-existing VMs. In the evaluation, we observe that AdaptIDC can greatly improve the shared memory utilization while performance draws near the fixed static shared page solution. [Copyright &y& Elsevier]

Applications in the cloud computing era have the emergent requirement of fast I/O support. Compared with a hard drive disk, a solid state disk (SSD) has low delay, low energy consumption, high throughput and other advantages. However, the semantics of an SSD cannot be recognized by current virtual machine monitors. The trim instruction, which plays an important role in space management of an SSD, cannot be passed to the underlying SSD device in a virtualized environment. So, how to bridge the semantics gap between the application layer and virtualization layer for SSD devices should be an important problem. We propose Vtrim to solve the above problem in this paper. Vtrim monitors the operations in the virtual machine and sends the SSD semantics to the Domain 0 immediately. And then the semantics are translated into the operations of Domain 0, which can trigger the SSD's local instructions. To improve the write performance in multiple guest operating systems, we set a Vtrim cache to buffer all instructions from the guests and flush them into an SSD in a well-scheduled way. Experiment results in the para-virtualization environments with Vtrim show that the random write performance is improved by even up to 100% and the average response time is reduced by up to 40%. [ABSTRACT FROM AUTHOR]

In a traditional virtualized cloud environment, using asynchronous I/O in the guest file system and synchronous I/O in the host file system to handle an asynchronous user disk write exhibits several drawbacks, such as performance disturbance among different guests and consistency maintenance across guest failures. To improve these issues, this paper introduces a novel disk I/O model for virtualized cloud system called HypeGear, where the guest file system uses synchronous operations to deal with the guest write request and the host file system performs asynchronous operations to write the data to the hard disk. A prototype system is implemented on the Xen hypervisor and our experimental results verify that this new model has many advantages over the conventional asynchronous-synchronous model. We also evaluate the overhead of asynchronous I/O at host, which is brought by our new model. The result demonstrates that it enforces little cost on host layer. [ABSTRACT FROM AUTHOR]

Memristor-based accelerator (MBA) has demonstrated its capability in accelerating matrix-vector multiplication (MVM) with high performance and energy efficiency. However, it is hard to determine whether and how well an application can benefit from MBAs in a heterogeneous computing architecture. In this article, we propose a simulation framework called MHSim to evaluate the energy efficiency and performance of applications running with both MBAs and CPUs. MHSim provides flexible system-level interfaces and circuit-level simulation models for designers to configure heterogeneous computing architectures. We design a general-purpose MBA which enables floating-point computation models for general matrix-matrix multiplication (GEMM). Our simulation framework can quantify the performance and energy efficiency of different MBA architectures for various applications. We validate our simulation framework with SPICE and evaluate the accuracy and performance of MBAs via several case studies. Experimental results demonstrate that the deviations of energy consumption and latency are only 0.47% and 0.49% on average compared with SPICE-based simulation. [ABSTRACT FROM AUTHOR]

Abstract: For cluster video servers, it is very important to design a good distributed storage system with high performance. One of the important issues in designing a good distributed storage system is how to store multimedia data on many storage nodes. This issue includes two topics: the scheme of splitting an entire file into many clips, and the storage of these clips on many nodes. We have designed a new multimedia data storage scheme for cluster video server. In the new system, a novel multimedia file splitting scheme, named Owl, and a clips striping scheme have been proposed. In contrast with traditional media data splitting schemes based on fixed space length and constant time length, Owl is addressed with the consideration of spatial and temporal information. This scheme Owl has universality for every media format encoded based on time and makes cluster video servers work efficiently. Besides, the scheme Owl is feasible and easy to implement. With the data splitting scheme and the clips striping scheme, cluster video servers have good performance. [Copyright &y& Elsevier]

Edge computing is highly recommended to support Mobile Crowdsensing (MCS) applications for sensing data processing. In this paper, we consider the MCS applications supported by the mobile phones of bus passengers, who transfer between different bus stations equipped with edge servers. The edge servers deployed with the corresponding MCS services can acquire and process the sensing data directly from the participants' sensors by device-to-device (D2D) communication. Therefore, with the help of edge services, it is desirable to deploy more MCS services to explore more D2D communications, without incurring cellular communication cost. Different stations are with different the number of passengers passing through, resulting in different benefits of edge service deployment. Taking the bus passenger mobility characteristics into consideration, we shall seek a tradeoff between the communication cost and service deployment cost to pursue overall cost efficiency. We first formulate the problem into a mixed integer linear programming model and then design a low-complexity heuristic algorithm. Performance evaluation results verify the high efficiency of our algorithm by the fact that it can much approach the optimal solution. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a chronic inflammatory joint disease. Traditional chinese medicine provides a resource for drug screening for OA treatment. Ginseng and the associated bioactive compound, ginsenosides, may reduce inflammation, which is considered a risk factor for the development of OA. Specifically, ginsenosides may exhibit anti-inflammatory and anti-oxidative stress activities, and inhibit extracellular matrix degradation by suppressing the NF-κB and MAPK signaling pathways. Notably, specific ginsenosides, such as compound K and Rk1, may physically interact with IκB kinase and inhibit the associated phosphorylation. Thus, ginsenosides exhibit potential as therapeutic candidates in the management of OA. However, the low water solubility limits the clinical applications of ginsenosides. Numerous effective strategies have been explored to improve bioavailability; however, further investigations are still required. [ABSTRACT FROM AUTHOR]

Hybrid memory systems composed of emerging non-volatile memory (NVM) and DRAM have drawn increasing attention in recent years. To fully exploit the advantages of both NVM and DRAM, a primary goal is to properly place application data on the hybrid memories. Previous studies have focused on page migration schemes to achieve higher performance and energy efficiency. However, those schemes all rely on online page access monitoring (costly), and data migration at the page granularity may cause additional overhead due to DRAM bandwidth contention and maintenance of cache/TLB consistency. In this article, we present Object-level memory Allocation and Migration (OAM) mechanisms for hybrid memory systems. OAM exploits a profiling tool to characterize objects’ memory access patterns at different execution phases of applications, and applies a performance/energy model to direct the initial static memory allocation and runtime dynamic object migration between NVM and DRAM. Based on our newly-developed programming interfaces for hybrid memory systems, application source codes can be automatically transformed via static code instrumentation. We evaluate OAM on an emulated hybrid memory system, and experimental results show that OAM can significantly reduce system energy-delay-product by 61 percent on average compared to a page-interleaving data placement scheme. It can also significantly reduce data migration overhead by 83 and 69 percent compared to the state-of-the-art page migration scheme CLOCK-DWF and 2PP, respectively, while improving application performance by up to 22 and 10 percent. [ABSTRACT FROM AUTHOR]

Circulating tumor DNA (ctDNA) is regarded as an ideal candidate biomarker for the non-invasive diagnosis of cancer. However, the lack of convenient and reliable detection methods for ctDNA restricts its clinical application. Herein, we developed a dual signal amplification strategy for sensitive detection of ctDNA based on hybridization chain reaction (HCR) and proximity hybridization-regulated CRISPR/Cas12a. The ctDNA initiates HCR through the continuous hybridization of two hairpin probes (H1 and H2), yielding long nicked double-stranded DNA nanowires composed of numerous split segments, which are successively connected to activate the trans -cleavage activity of CRISPR/Cas12a. In this case, the doubly labeled single-stranded DNA reporter can be cleaved to produce a strong fluorescent signal. Owing to the dual amplification of HCR and CRISPR/Cas12a, this strategy exhibits high sensitivity toward ctDNA with a low detection limit of 5.43 fM. Moreover, the proposed method was successfully applied for ctDNA detection in serum samples with satisfactory results, which has great potential in the clinical diagnosis of cancer. [Display omitted] • A dual signal amplification strategy is developed based on HCR and proximity hybridization-regulated CRISPR/Cas12a. • The proposed strategy exhibits high sensitivity for ctDNA detection with a detection limit down to 5.43 fM. • This method can be applied for the determination of target ctDNA in human serum samples. • This work may open the door to joint nucleic acid amplification techniques and CRISPR/Cas12a by proximity hybridization. [ABSTRACT FROM AUTHOR]

Bone and cartilage disorders are the leading causes of musculoskeletal disability. There is no absolute cure for all bone and cartilage disorders. The exploration of natural compounds for the potential therapeutic use against bone and cartilage disorders is proving promising. Among these natural chemicals, naringin, a flavanone glycoside, is a potential candidate due to its multifaceted pharmacological activities in bone and cartilage tissues. Emerging studies indicate that naringin may promote osteogenic differentiation, inhibit osteoclast formation, and exhibit protective effects against osteoporosis in vivo and in vitro. Many signaling pathways, such as BMP-2, Wnt/β-catenin, and VEGF/VEGFR, participate in the biological actions of naringin in mediating the pathological development of osteoporosis. In addition, the anti-inflammatory, anti-oxidative stress, and anti-apoptosis abilities of naringin also demonstrate its beneficial effects against bone and cartilage disorders, including intervertebral disc degeneration, osteoarthritis, rheumatoid arthritis, bone and cartilage tumors, and tibial dyschondroplasia. Naringin exhibits protective effects against bone and cartilage disorders. However, more efforts are still needed due to, at least in part, the uncertainty of drug targets. Further biological and pharmacological evaluations of naringin and its applications in bone tissue engineering, particularly its therapeutic effects against osteoporosis, might result in developing potential drug candidates. [ABSTRACT FROM AUTHOR]

To support the ever-increasing data traffic demands, the Internet has been experiencing a rapid growth in recent decades. Effective and efficient monitoring is highly needed in order to properly manage such complex infrastructure. It is of theoretical and practical significance to derive network device importance (i.e., node rank) for resource utilization optimization, user experience improvement, and security enhancement. Recent development in the Internet infrastructure has introduced prosperous in-network computation resources across the network. To exploit such distributed resources, in this article, we propose a core-graphbased framework, called FRank, for fast node ranking algorithms, which accelerates convergence and reduces communication cost by converting most inter-partition state change propagations into intra-partition ones. We have implemented FRank in a cluster to validate its correctness and efficiency. The experiment results demonstrate that at the least FRank reduces the execution time of existing cutting-edge methods by 30.2 percent with 43.5 percent less communication cost. [ABSTRACT FROM AUTHOR]

Alkaline phosphatase (ALP) is regarded as an emerging biomarker and therapeutic target for various diseases. Herein, we developed a DNAzyme-regulated CRISPR/Cas12a cascade signal amplification strategy for sensitive and selective detection of ALP activity and inhibition. In this assay, the substrate strand of Cu2+-specific DNAzyme was designed as the DNA activator for Cas12a. The substrate strand would be cleaved into two fragments when ALP-catalyzed hydrolysis reaction disabled the complexation between Cu2+ and pyrophosphate (PPi). In this case, the cleaved sequences could not further bind to the Cas12a-crRNA complex to activate the trans -cleavage activity of CRISPR/Cas12a, which would hamper the cleavage of doubly labeled single-stranded DNA (ssDNA) fluorescence reporter, and thus a turn-off fluorescence signal. The cascade signal amplification strategy greatly improved the detection sensitivity for ALP activity, with a detection limit as low as 0.04 U/L. Moreover, the proposed method was successfully applied for ALP activity detection in serum samples and ALP inhibitory capability evaluation. This method overcomes the shortcoming of conventional methods that show unsatisfactory sensitivity and are susceptible to interfering substances, which might hold great promise in clinical diagnosis and biomedical research. [Display omitted] • A sensitive fluorescent strategy is developed based on DNAzyme-regulated CRISPR/Cas12a cascade signal amplification. • The fluorescent sensing strategy exhibits high sensitivity and selectivity for detection of ALP activity in serum samples. • The proposed strategy can be applied for evaluating inhibition efficiency and screening ALP inhibitors. • This work broadens the applications of CRISPR/Cas-based sensing systems in biomedical research. [ABSTRACT FROM AUTHOR]

Dynamic graph convolutional networks (DGCNs) have emerged as an effective approach to analyzing graph data that is constantly changing. The typical DGCNs incorporate not only graph convolutional networks (GCNs) to extract the structural information but also with recurrent neural networks (RNNs) to capture the temporal information from evolving graph data. These two alternative execution kernels of DGCNs impose unique architecture challenges for both types of kernels to be implemented efficiently. The presence of complex execution patterns of DGCNs renders existing architectures unsuitable. In this article, we present the first DGCN accelerator with an integrated architecture, named ReaDy, to accelerate DGCNs based on emerging PIM-featured ReRAM architectures. ReaDy is novel with an integrated architecture that enables running the GCN and RNN kernels of DGCNs simultaneously. Specifically, ReaDy is equipped with a redundancy-free scheduling mechanism to alleviate intrinsic dynamic irregularity for the GCN kernel, improving hardware utilization. In addition, ReaDy also includes a locality-aware dataflow strategy to exploit the inherent intervertex data locality for the RNN kernel, reducing superfluous data accesses to vertices and weight parameters. In a holistic view, ReaDy further enhances the entire system via an interkernel pipeline to reduce the off-chip accesses of intermediate results, boosting the overall efficiency of DGCNs significantly. Compared to the state-of-the-art software framework, PyGT, running on Intel Xeon E5-2680v4 CPU and NVIDIA Ampere A100 GPU, ReaDy achieves the average speedups of $955\times $ and $27.33\times $ , and the average energy savings of 1 $093\times $ and $80.21\times $ , respectively. In addition, ReaDy outperforms ReFlip-ERA, which is obtained by combining a state-of-the-art GCN accelerator ReFlip and RNN accelerator ERA-LSTM, by an average speedup of $8.30\times $ and an average energy saving of $7.29\times $. [ABSTRACT FROM AUTHOR]

Pruning deep neural networks (DNNs) can reduce the model size and thus save hardware resources of a resistive-random-access-memory (ReRAM)-based DNN accelerator. For the tightly coupled crossbar structure, existing ReRAM-based pruning techniques prune the weights of a DNN in a structured manner, thereby attaining low pruning ratios. This article presents a novel pruning technique, SegPrune, for pruning the weights of a DNN flexibly on crossbar architectures in order to maximize the pruning ratio achieved while preserving crossbar efficiency. We observe that different filters of a weight matrix share a large number of matrix subcolumns (in the same rows), called segments, that can be pruned by using the same segment shape in the sense that the weights at the same column position of these segments are either simultaneously accuracy-sensitive (and should thus be reserved) or simultaneously accuracy-insensitive (and can thus be pruned). Due to the bit-line exchangeability in the crossbar, segments with the same pruning shape can be assembled together into the same crossbar to ensure crossbar execution efficiency. We propose a projection-based shape voting algorithm to select suitable segment shapes to drive the weight pruning process. Accordingly, we also introduce a low-overhead data path that can be easily integrated into any existing ReRAM-based DNN accelerator, achieving a high pruning ratio and a high execution efficiency. Our evaluation shows that SegPrune outperforms the state-of-the-art, Hybrid-P, and FORMAS, by up to $14.6\times $ and $3.6\times $ in pruning ratio, $13.9\times $ and $3.4\times $ in inference speedup, and $12.5\times $ and $3.1\times $ in energy reduction, respectively, while achieving an even higher accuracy at the cost of less than 0.27% extra hardware area overhead. [ABSTRACT FROM AUTHOR]

Processing-In-Memory(PIM) has emerged as a high-performance and energy-efficient computing paradigm for accelerating convolutional neural network (CNN) applications. Resistive random access memory (ReRAM) has been widely used in PIM architectures due to its extremely high efficiency for accelerating matrix-vector multiplications through analog computing. However, because CNN training usually requires high-precision computation in the backward propagation (BP) stage, the limited precision of analog PIM accelerators impedes their adoption in CNN training. In this article, we propose ReHy, a hybrid PIM accelerator to support CNN training in ReRAM arrays. It is composed of Analog PIM (APIM) and Digital PIM (DPIM) modules. ReHy uses APIM to accelerate the feed-forward propagation (FP) stage for high performance, and DPIM to process the BP stage for high accuracy. We exploit the capability of ReRAM for Boolean logic operations to design the DPIM architecture. Particularly, we design floating-point multiplication and addition operators to support matrix multiplications in ReRAM arrays. We also propose a performance model to offload high-precision matrix multiplications to DPIM according to the data parallelism. Experimental results show that ReHy can speed up CNN training by 48.8× and 2.4×, and reduce energy consumption by 35.1× and 2.33×, compared with CPU/GPU architectures (baseline) and the state-of-the-art FloatPIM, respectively. [ABSTRACT FROM AUTHOR]

The mechanism of OH radical with guanine-cytosine (G.C) base pair has been explored at the B3LYP/DZP++ level of density functional theory (DFT). Structures perturbations along the of G.C base pair cause strain in the pairing and double-strand breaks in DNA. Seven possible reactions are exothermic, and the reaction energy decreases in the order of G.CC4 > GC5.C > GC2.C > GC4.C > G.CC5 > G.CC6 > GC8.C. The reactions at G.CC5 and GC8.C sites appear to be barrierless, and the sequence of the barrier energy is G.CC4 > GC4.C > GC2.C > GC5.C > G.CC6 > G.CC5 ~ GC8.C. The results indicate that at GC8.C, G.CC5 and G.CC6 are more thermodynamically and kinetically favorable than other sites in G.C base pair. Considering the solvent effects by using the polarizable continuum model, the stabilities of all the compounds are increased significantly. Little change is taken place on the data of the reaction energies and barrier energies. Their sequences and the stability order follow the same trends like them in gas phase. The fluctuation of natural bond orbital charge further confirms that the reactions are exothermic. And transient spectra computed with the time-dependent density functional theory (TD-DFT) match well with the previous experimental and theoretical reports. Our deduced mechanism is in good agreement with the experimentally observed hydroxylated adducts. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In virtualization environments, static memory allocation for virtual machines (VMs) can lead to severe service level agreement (SLA) violations or inefficient use of memory. Dynamic memory allocation mechanisms such as ballooning and memory hotplug were proposed to handle the dynamics of memory demands. However, these mechanisms so far have not been quantitatively or comparatively studied. In this paper, we first develop a runtime system called U-tube, which provides a framework to adopt memory hotplug or ballooning for dynamic memory allocation. We then implement fine-grained memory hotplug in Xen. We demonstrate the effectiveness of U-tube for dynamic memory management through two case studies: dynamic memory balancing and memory overcommitment. With these two case studies, we make a quantitative comparison between memory hotplug and ballooning. The experiments show that there is no absolute winner for different scenarios. Our findings can be very useful for practitioners to choose the suitable dynamic memory management techniques in different scenarios. [ABSTRACT FROM AUTHOR]

It is meaningful to use a little energy to obtain more performance improvement compared with the increased energy. It also makes sense to relax a small quantity of performance restriction to save an enormous amount of energy. Trading a small amount of energy for a considerable sum of performance or vice versa is possible if the relativities between performance and energy of parallel programs are exactly known. This work studies the relativities by recording the performance speedup and energy consumption of parallel programs when the number of cores on which programs run are changed. We demonstrate that the performance improvement and the increased energy consumption have a linear negative correlation.In addition, these relativities can guide us to do performance-energy adaptation under two assumptions. Our experiments show that the average correlation coefficients between performance and energy are higher than 97 %. Furthermore, it can be found that exchanging less than 6 % performance loss for more than 37 % energy consumption is feasible and vise versa. [ABSTRACT FROM AUTHOR]

More and more unstructured data are produced and consumed over network. How to maintain these data and improve the availability and scalability of the storage systems has become a considerable challenge. Although some NoSQL systems such as Dynamo, Cassandra, MongoDB have provided different advantages for unstructured data management, no one can provide flexible query functions like MongoDB, meanwhile guarantee the availability and scalability as Cassandra simultaneously. This paper presents a new high available distributed storage system called MyStore based on an optimized clustered MongoDB for unstructured data. Consistent hash is used to distribute data on multiple MongoDB nodes by applying virtual node method. NWR mode is applied to provide automatic backup operation and guarantee data consistency. And a gossip protocol is taken for exchanging information of failures in the system. Moreover, a user-friendly interface module and an efficient cache module are designed for improving the usability of the system. Based on above strategies, the system can realize high availability for unstructured data storage, while providing complex query functions like rational databases. Moreover, it is applied in a multi-discipline virtual experiment platform named VeePalms that has run practically. Experimental evaluation shows that the methodology is powerful enough not only to enhance the data availability, but also to improve the server's scalability. [ABSTRACT FROM AUTHOR]

Abstract: Desktop virtualization is a general solution for providing users with various working environments on a single physical machine. It is typically based on the virtual machine (VM) technology, which can provide smart sharing policies on the scarce hardware. Compared with native computing environments, however, VM may sacrifice the performance, especially in the I/O subsystem. Although certain methods have been proposed to improve the performance of networking and graphic processing, the performance of disk I/O is almost ignored. In this paper, we propose two methods to reduce the extra overhead on Xen hypervisor at different layers of its disk protocol stack. The experimental results show that on the average 21% of unnecessary CPU cycles can be saved from the Xen hypervisor, and when a high performance disk device is applied in the system, our proposed optimization techniques can improve the overall disk I/O performance by 15.6%–36.6% for all benchmarks used in the experiments. Finally, by evaluating our methods in a practical system of desktop virtualization, user-applications can achieve an improvement of 15.9%–23.7% over the original Xen hypervisor. [Copyright &y& Elsevier]

Abnormal levels of reduced glutathione (GSH) and glutathione reductase (GR) are usually related to a variety of diseases, so it is of great significance to determine the GSH concentration and GR activity. We herein develop a smartphone-assisted colorimetric biosensor for the detection of GSH and GR activity in human serum and mouse liver using hemin/G-quadruplex DNAzyme. Firstly, an obvious color change from colorless to green can be observed, owing to the high peroxidase-like activity of hemin/G-quadruplex DNAzyme toward 2,2′-azino-bis(3-ethylbenzothiozoline-6-sulfonic acid) (ABTS). With the addition of GSH or GR, the H2O2-mediated oxidation of ABTS catalyzed by hemin/G-quadruplex DNAzyme is significantly inhibited, resulting in remarkable color fading. Therefore, the detection of GSH and GR activity can be achieved by observing the color transition or measuring the absorbance at 420 nm. The detection limit was estimated to be as low as 0.1 μM and 10 μU/mL for GSH and GR, respectively. More interestingly, the RGB values of the sensing system can be identified by the smartphone application (APP, color collect), which makes it an ideal format for on-site determination and point-of-care testing (POCT). In addition, the proposed method shows excellent selectivity and acceptable applicability for the determination of GSH concentration and GR activity in human serum samples and mouse liver tissues, which might hold great application potential in clinical diagnosis and drug screening. [ABSTRACT FROM AUTHOR]

Current DRAM-based memory systems face the scalability challenges in terms of memory density, energy consumption, and monetary cost. Hybrid memory architectures composed of emerging nonvolatile memory (NVM) and DRAM is a promising approach to large-capacity and energy-efficient main memory. However, hybrid memory systems pose a new challenge to on-chip cache management due to the asymmetrical penalty of memory access to DRAM and NVM in case of cache misses. Cache hit rate is no longer an effective metric for evaluating memory access performance in hybrid memory systems. Current cache replacement policies that aim to improve the cache hit rate are not efficient either. In this article, we take into account the asymmetry of the cache miss penalty on DRAM and NVM, and advocate a more general metric, average memory access time (AMAT), to evaluate the performance of hybrid memories. We propose a miss penalty aware LRU-based cache replacement policy (MALRU) for hybrid memory systems. MALRU is aware of the source (DRAM or NVM) of missing blocks and preserves high-latency NVM blocks as well as low-latency DRAM blocks with good temporal locality in the last level cache. The experimental results show that MALRU can improve system performance by up to 22.8% and 13.1%, compared to LRU and the state-of-the-art hybrid memory aware cache partitioning technique policy, respectively. [ABSTRACT FROM AUTHOR]

The huge amount of data enforces great pressure on the processing efficiency of database systems. By leveraging the in-situ computing ability of emerging nonvolatile memory, processing-in-memory (PIM) technology shows great potential in accelerating database operations against traditional architectures without data movement overheads. In this article, we introduce ReSQM, a novel ReCAM-based accelerator, which can dramatically reduce the response time of database systems. The key novelty of ReSQM is that some commonly used database queries that would be otherwise processed inefficiently in previous studies can be in-situ accomplished with massively high parallelism by exploiting the PIM-enabled ReCAM array. ReSQM supports some typical database queries (such as SELECTION, SORT, and JOIN) effectively based on the limited computational mode of the ReCAM array. ReSQM is also equipped with a series of hardware-algorithm co-designs to maximize efficiency. We present a new data mapping mechanism that allows enjoying in-situ in-memory computations for SELECTION operating upon intermediate results. We also develop a count-based ReCAM-specific algorithm to enable the in-memory sorting without any row swapping. The relational comparisons are integrated for accelerating inequality join by making a few modifications to the ReCAM cells with negligible hardware overhead. The experimental results show that ReSQM can improve the (energy) efficiency by $611\times $ ($193\times $), $19\times $ ($17\times $), $59\times $ ($43\times $), and $307\times $ ($181\times $) in comparison to a 10-core Intel Xeon E5-2630v4 processor for SELECTION, SORT, equi-join, and inequality join, respectively. In contrast to state-of-the-art CMOS-based CAM, GPU, FPGA, NDP, and PIM solutions, ReSQM can also offer $2.2\times 39\times $ speedups. [ABSTRACT FROM AUTHOR]

Nowadays emerging Non-Volatile Memory (NVM) is introduced to remedy the shortages of current DRAM-based main memory, but limited write en-durance of NVM would severely restrict memory system. Many techniques are proposed to extend NVM lifetime based on DRAM and NVM hybrid memory. However, most of them cannot obtain satisfactory NVM lifetime and performance at the same time for various workloads, because they fail to deal well with the conict between reducing writebacks to NVM and improving DRAM access hit ratio. In order to solve this problem, we propose a Victim-Aware Cache Policy named VAIL for DRAM and NVM hybrid memory system. VAIL uti-lizes the property of victim pages to estimate and predict the impact of clean and dirty page replacement on writebacks to NVM and DRAM access hit ratio. Then, pick the page which has the minimal negative impact for replacement. By doing so, VAIL can reduce writebacks to NVM and achieve high DRAM access hit ratio simultaneously. It outperforms the state-of-the-art by 1.29x higher performance and leads to 11.7% less NVM wear-out, according to our evaluation with HPC, graph computing, and machine learning workloads. [ABSTRACT FROM AUTHOR]

• Extra-small and ultra-stable sugar-substituted NHC stabilized AuNPs were synthesized by one-pot method. • Chiral AuNPs were characterized by UV–Vis, NMR, HR-TEM, TGA, CD, FT-IR, and XPS spectroscopy. • Chiral AuNPs with core diameters ranging from 2.3 to 3.8 nm remained ultra-stable against glutathione. • Chiral AuNPs exhibited excellent antibacterial activity against bacterium Xanthomonas citri susp. citri. • Chiral AuNPs exhibited excellent catalytic activity towards the reduction of nitroarenes. N -heterocyclic carbenes (NHCs) have evidenced wonderful alternatives as surface ligands, while synthetic challenges have limited their application in chiral gold nanoparticles (AuNPs). To avoid using difficult-to-synthesize and light-sensitive NHC-Au(I) complex, we synthesized four glucopyranoside-based benzimidazolium bromide salts (1a – 1d-Br) and their corresponding haloaurate(III) salts (2a – 2d-AuCl 4). Then, the first-generation sugar-substituted NHCs stabilized extra-small and ultra-stable chiral AuNPs (Au@NHCs-sugar NPs, 3a – 3d-AuNPs) was synthesized though two mild, efficient, and operation-friendly one-pot chemical reduction methods. The obtained 3a – 3d-AuNPs were characterized by UV–Vis, NMR, HR-TEM, TGA, CD, FT-IR, and XPS spectroscopy. The designed 3a – 3d-AuNPs with core diameters ranging from 2.3 to 3.8 nm remained stable against glutathione for up to 24 h over a wide temperature range of 95 to −78 °C, and over 4 years in air. The absence of NC H N proton in 1H NMR and FT-IR of 3a – 3d-AuNPs confirmed the formation of NHC-Au bond, and clearly showed that all acetyl groups, benzimidazolium heterocycle, and N -substituents maintained. XPS analyses revealed the presence of Au(0) and Au(I) in all Au@NHCs-sugar NPs. This result leads us to postulate that 3a – 3d-AuNPs exhibit a monolayer of Au(I) surrounding an Au(0) core. These designed 3a – 3d-AuNPs were studied for their self-assembly behavior, antibacterial activity against bacterium Xanthomonas citri susp. citri (Xcc), and catalytic activity towards the reduction of nitroarenes. All of them demonstrated strong antibacterial activity against Xcc with minimum inhibitory concentration value <0.03125 mg·mL−1. [ABSTRACT FROM AUTHOR]

Hardware accelerated inference is a promising solution for exploiting graph convolutional networks (GCN) in latency-sensitive applications. Existing accelerators overlook an important barrier to widespread adoption: the input data (i.e., weighted graphs) of GCN inference diverge from scale and sparsity, causing the accelerators optimized for an array of graphs to lose efficiency on other graphs. This paper presents a reconfigurable GCN inference accelerator, VIDGCN, that switches between all possible GCN inference computation schemes to realize timely inference for all input graphs. VIDGCN incorporates an analytical performance model and a reconfigurable hardware design. The performance model allows users to find the optimal computation scheme for any given input graph. The hardware design reuses all the computation units under all computation schemes, and only distributes the data to the units in different ways. Evaluation on seven real-world graphs shows that VIDGCN outperforms state of the art, SGCNAX, by 1.79 × , and consistently yields the ideal amount of memory accesses. [ABSTRACT FROM AUTHOR]

Big data analytics has attracted close attention from both industry and academic because of its great benefits in cost reduction and better decision making. As the fast growth of various global services, there is an increasing need for big data analytics across multiple data centers (DCs) located in different countries or regions. It asks for the support of a cross-DC data processing platform optimized for the geo-distributed computing environment. Although some recent efforts have been made for geo-distributed big data analytics, they cannot guarantee predictable job completion time, and would incur excessive traffic over the inter-DC network that is a scarce resource shared by many applications. In this paper, we study to minimize the inter-DC traffic generated by MapReduce jobs targeting on geo-distributed big data, while providing predicted job completion time. To achieve this goal, we formulate an optimization problem by jointly considering input data movement and task placement. Furthermore, we guarantee predictable job completion time by applying the chance-constrained optimization technique, such that the MapReduce job can finish within a predefined job completion time with high probability. To evaluate the performance of our proposal, we conduct extensive simulations using real traces generated by a set of queries on Hive. The results show that our proposal can reduce 55 percent inter-DC traffic compared with centralized processing by aggregating all data to a single data center. [ABSTRACT FROM AUTHOR]

In this paper, two new iridium(III) complexes [Ir(ppy) 2 (CBIP)](PF 6) (ppy = 2-phenylpyridine, CBIP = 2-(4′-chloro-(1,1′-biphenyl))-1 H -imidazo[4,5-f][ 1 , 10 ]phenanthroline) (Ir1) and [Ir(piq) 2 (CBIP)](PF 6) (piq = 1-phenylisoquinoline) (Ir2) were synthesized and characterized. The anticancer activity of the complexes against cancer A549, HepG2, SGC-7901, BEL-7402, HeLa and LO2 cells was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Unexpectedly, the complexes exhibit no or low cytotoxic activity toward the selected cancer cells. To increase the anticancer activity, complexes Ir1 and Ir2 were encapsulated into the liposome to form Ir1lipo and Ir2lipo , while Ir1lipo and Ir2lipo show high cytotoxic efficacy against BEL-7402, SGC-7901 and HeLa cells and Ir2lipo displays moderate cytotoxic activity against A549 and HepG2. The anticancer mechanism was explored through wound healing, cell cycle arrest, apoptosis, the change of mitochondrial membrane potential and antitumor activity in vivo. The antitumor in vivo showed that Ir1Lipo (3.9 mg/kg) exhibited significant antitumor activity with an inhibitory rate of 62.16%. Additionally, the expression of B-cell lymphoma-2 family proteins was studies by western blotting analysis. The results demonstrate that Ir1lipo and Ir2lipo induce apoptosis in BEL-7402 cells via endoplasmic reticulum stress-mitochondrial pathway. [Ir(ppy) 2 (CBIP)](PF 6) (ppy = 2-phenylpyridine, Ir1), [Ir(piq) 2 (CBIP)](PF 6) (piq = 1-phenylisoquinoline, CBIP = 2-(4′-chloro-(1,1′-biphenyl))-1 H -imidazo[4,5-f] [1,10]phenanthroline, Ir2), complex-loaded liposomes Ir1lipo and Ir2lipo were synthesized. The anticancer activity in vitro and in vivo of the complexes and their liposomes were evaluated. [Display omitted] • Three iridium(III) complexes and complex-loaded liposomes were synthesized. • The cytotoxicity in vitro and in vivo of the complexes and liposomes was studied. • The apoptosis and cell cycle arrest were performed. • The location of the complexes at the endoplasm reticulum and mitochondria was explored. • The expression of B-cell lymphoma-2 family proteins was assayed. [ABSTRACT FROM AUTHOR]