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File layout of array data is a critical factor that effects the behavior of storage caches, and has so far taken not much attention in the context of hierarchical storage systems. The main contribution of this paper is a compiler-driven file layout optimization scheme for hierarchical storage caches. This approach, fully automated within an optimizing compiler, analyzes a multi-threaded application code and determines a file layout for each disk-resident array referenced by the code, such that the performance of the target storage cache hierarchy is maximized. We tested our approach using 16 I/O intensive application programs and compared its performance against two previously proposed approaches under different cache space management schemes. Our experimental results show that the proposed approach improves the execution time of these parallel applications by 23.7% on average. [ABSTRACT FROM AUTHOR]

File layout of array data is a critical factor that effects the behavior of storage caches, and has so far taken not much attention in the context of hierarchical storage systems. The main contribution of this paper is a compiler-driven file layout optimization scheme for hierarchical storage caches. This approach, fully automated within an optimizing compiler, analyzes a multi-threaded application code and determines a file layout for each disk-resident array referenced by the code, such that the performance of the target storage cache hierarchy is maximized. We tested our approach using 16 I/O intensive application programs and compared its performance against two previously proposed approaches under different cache space management schemes. Our experimental results show that the proposed approach improves the execution time of these parallel applications by 23.7% on average. [ABSTRACT FROM AUTHOR]

Drawing parallels to the rise of general purpose graphical processing units (GPGPUs) as accelerators for specific high-performance computing (HPC) workloads, there is a rise in the use of non-volatile memory (NVM) as accelerators for I/O-intensive scientific applications. However, existing works have explored use of NVM within dedicated I/O nodes, which are distant from the compute nodes that actually need such acceleration. As NVM bandwidth begins to out-pace point-to-point network capacity, we argue for the need to break from the archetype of completely separated storage.Therefore, in this work we investigate co-location of NVM and compute by varying I/O interfaces, file systems, types of NVM, and both current and future SSD architectures, uncovering numerous bottlenecks implicit in these various levels in the I/O stack. We present novel hardware and software solutions, including the new Unified File System (UFS), to enable fuller utilization of the new compute-local NVM storage. Our experimental evaluation, which employs a real-world Out-of-Core (OoC) HPC application, demonstrates throughput increases in excess of an order of magnitude over current approaches. [ABSTRACT FROM AUTHOR]

The ability of sensor nodes to enter a low power sleep mode is very useful for extending network longevity. We show how adversary nodes can exploit clustering algorithms to ensure their selection as cluster heads for the purpose of launching attacks that prevent victim nodes from sleeping. We present two such attacks: the barrage attack and the sleep deprivation attack . The barrage attack bombards victim nodes with legitimate requests, whereas the sleep deprivation attack makes requests of victim nodes only as often as in necessary to keep the victims awake. We show that while the barrage attack causes its victims to spend slightly more energy, it is more easily detected and requires more effort on behalf of the attacker. Thus, we have focused our research on the sleep deprivation attack. Our analysis indicates that this attack can nullify any energy savings obtained by allowing sensor nodes to enter sleep mode. We also analyze three separate methods for mitigating this attack: the random vote scheme, the round robin scheme, and the hash-based scheme. We have evaluated these schemes based upon their ability to reduce the adversary's attack, the amount of time required to select a cluster head, and the amount of energy required to perform each scheme. We have found that of the three clustering methods analyzed, the hash-based scheme is the best at mitigating the sleep deprivation attack. [ABSTRACT FROM AUTHOR]