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Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a challenging task due to significant irregular memory references and workload imbalance across GPUs. These challenges are particularly compounded in the case of Sparse Triangular Solver (SpTRSV), which introduces additional complexity of two-dimensional computation dependencies among subsequent computation steps. Dependency information may need to be exchanged and shared among GPUs, thus warranting for efficient memory allocation, data partitioning, and workload distribution as well as fine-grained communication and synchronization support. In this work, we focus on designing algorithm for SpTRSV in a single-node, multi-GPU setting. We demonstrate that directly adopting unified memory can adversely affect the performance of SpTRSV on multi-GPU architectures, despite linking via fast interconnect like NVLinks and NVSwitches. Alternatively, we employ the latest NVSHMEM technology based on Partitioned Global Address Space programming model to enable efficient fine-grained communication and drastic synchronization overhead reduction. Furthermore, to handle workload imbalance, we propose a malleable task-pool execution model which can further enhance the utilization of GPUs. By applying these techniques, our experiments on the NVIDIA multi-GPU supernode V100-DGX-1 and DGX-2 systems demonstrate that our design can achieve an average of 3.53 × (up to 9.86 ×) speedup on a DGX-1 system and 3.66 × (up to 9.64 ×) speedup on a DGX-2 system with four GPUs over the Unified-Memory design. The comprehensive sensitivity and scalability studies also show that the proposed zero-copy SpTRSV is able to fully utilize the computing and communication resources of the multi-GPU systems.

Graphs play a key role in data analytics. Graphs and the software systems used to work with them are highly diverse. Algorithms interact with hardware in different ways and which graph solution works best on a given platform changes with the structure of the graph. This makes it difficult to decide which graph programming framework is the best for a given situation. In this paper, we try to make sense of this diverse landscape. We evaluate five different frameworks for graph analytics: SuiteS-parse GraphBLAS, Galois, the NWGraph library, the Graph Kernel Collection, and GraphIt. We use the GAP Benchmark Suite to evaluate each framework. GAP consists of 30 tests: six graph algorithms (breadth-first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) on five graphs. The GAP Benchmark Suite includes high-performance reference implementations to provide a performance baseline for comparison. Our results show the relative strengths of each framework, but also serve as a case study for the challenges of establishing objective measures for comparing graph frameworks.

Triangles are the simplest non-trivial subgraphs and triangle counting is used in a number of different applications. The order in which vertices are processed in triangle counting strongly effects the amount of work that needs to be done (and thus the overall performance). Ordering vertices by degree has been shown to be one particularly effective ordering approach. However, for graphs with skewed degree distributions (such as power-law graphs), ordering by degree effects the distribution of work; parallelization must account for this distribution in order to balance work among workers. In this paper we provide an in-depth analysis of the ramifications of degree-based ordering on parallel triangle counting. We present approach for partitioning work in triangle counting, based on cyclic distribution and some surprisingly simple C++ implementations. Experimental results demonstrate the effectiveness of our approach, particularly for power-law (and social network) graphs.

Today's data-driven analytics and machine learning workload have been largely driven by the General-Purpose Graphics Processing Units (GPGPUs). To accelerate dense matrix multiplications on the GPUs, Tensor Core Units (TCUs) have been introduced in recent years. In this paper, we study linear-algebra-based and vertex-centric algorithms for various graph kernels on the GPUs with an objective of applying this new hardware feature to graph applications. We identify the potential stages in these graph kernels that can be executed on the Tensor Core Units. In particular, we leverage the reformulation of the reduction and scan operations in terms of matrix multiplication [1] on the TCUs. We demonstrate that executing these operations on the TCUs, available inside different graph kernels, can assist in establishing an end-to-end pipeline on the GPGPUs without depending on hand-tuned external libraries and still can deliver comparable performance for various graph analytics.

In this paper, we discuss our experience in implementing homology computation, in particular the Betti number calculations in Chapel hypergraph Library (CHGL). Given a dataset represented as a hypergraph, a Betti number for a particular dimension k indicates how many k-dimensional `voids' are present in the dataset. Computing the Betti numbers involve various array-centric and linear algebra operations. We demonstrate that implementing these operations in Chapel is both concise and intuitive. In addition, we show that Chapel provides language constructs for implementing parallel and distributed execution of the linear algebra kernels with minimal effort. Syntactically, Chapel provides succinctness of Python, while delivering comparable and better performance than C++-based and Julia-based packages for calculating the Betti numbers respectively.

We report on the use of novel mathematical methods in hypergraph analytics over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech’s ActiveDNS repository as hypergraphs allows us to fully represent the interactions between collections of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities: HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs; while on the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.

In this paper, we propose a distributed, unordered, label-correcting distance-1 Grundy (vertex) coloring algorithm, namely, Distributed Control (DC) coloring algorithm. Our algorithm eliminates the need for vertex-centric barriers and global synchronization for color refinement, relying only on atomic operations and local termination detection to update vertex color. DC proceeds optimistically, correcting the colors asynchronously as the algorithm progresses and depends on local ordering of tasks to minimize the execution of sub-optimal work. We implement our DC coloring algorithm and the well-known Jones-Plassmann algorithm and compare their performance with 4 different types of standard RMAT graphs and real-world graphs. We show that the elimination of waiting time of global and vertex-centric barriers and investing this time for local ordering leads to improved scaling for graphs with prominent power-law characteristics and densely interconnected local subgraphs.

Designing a scalable algorithm for community detection is challenging due to the simultaneous need for both high performance and quality of solution. We propose a new distributed algorithm for community detection based on a novel Label Propagation algorithm. The algorithm is inspired by the direction optimization technique in graph traversal algorithms, relies on the use of frontiers, and alternates between abstractions called label push and label pull. This organization creates flexibility and affords us with opportunities for balancing performance and quality of solution. We implement our algorithm in distributed memory with the active-message based asynchronous many-task runtime AM++. We experiment with two seeding strategies for the initial seeding stage, namely, random seeding and degree seeding. With the Graph Challenge dataset, our distributed implementation, in conjunction with the runtime support, detects the communities in graphs having 20 million vertices in less than one second while achieving reasonably high quality of solution.

The Partitioned Global Address Space (PGAS) programming model can be implemented either with programming language features or with runtime library APIs, each implementation favoring different aspects (e.g., productivity, abstraction, flexibility, or performance). Certain language and runtime features, such as collectives, explicit and asynchronous communication primitives, and constructs facilitating overlap of communication and computation (such as futures and conjoined futures) can enable better performance and scaling for irregular applications, in particular for distributed graph analytics. We compare graph algorithms in one of each of these environments: the Chapel PGAS programming language and the the UPC++ PGAS runtime library. We implement algorithms for breadth-first search and triangle counting graph kernels in both environments. We discuss the code in each of the environments, and compile performance data on a Cray Aries and an Infiniband platform. Our results show that the library-based approach of UPC++ currently provides strong performance while Chapel provides a high-level abstraction that, harder to optimize, still provides comparable performance.

Because they were developed for optimal sequential complexity, classical graph algorithms as found in textbooks have strictly-defined orders of operations. Enforcing a prescribed order of operations, or even an approximate order, in a distributed memory setting requires significant amounts of synchronization, which in turn can severely limit scalability. As a result, new algorithms are typically required to achieve scalable performance, even for solving well-known graph problems. Yet, even in these cases, parallel graph algorithms are written according to parallel programming models that evolved for, e.g., scientific computing, and that still have inherent, and scalability-limiting, amounts of synchronization. In this paper we present a new approach to parallel graph algorithms: synchronization-avoiding algorithms. To eliminate synchronization and its associated overhead, synchronization-avoiding algorithms perform work in an unordered and fully asynchronous fashion in such a way that the result is constantly refined toward its final state. "Wasted" work is minimized by locally prioritizing tasks using problem-dependent task utility metrics. We classify algorithms for graph applications into two broad categories: algorithms with monotonic updates (which evince global synchronization) and algorithms with non-monotonic updates (which evince vertex-centric synchronization). We apply our approach to both classes and develop novel, synchronization-avoiding algorithms for solving exemplar problems: SSSP and connected components for the former, graph coloring for the latter. We demonstrate that eliminating synchronization in conjunction with effective scheduling policies and optimizations in the runtime results in improved scalability for both classes of algorithms.

Performance of distributed graph algorithms can benefit greatly by forming rapport between algorithmic abstraction and the underlying runtime system that is responsible for scheduling work and exchanging messages. However, due to their dynamic and irregular nature of computation, distributed graph algorithms written in different programming models impose varying degrees of workload pressure on the runtime. To cope with such vastly different workload characteristics, a runtime has to make several trade-offs. One such trade-off arises, for example, when the runtime scheduler has to choose among alternatives such as whether to execute algorithmic work, or progress the network by probing network buffers, or throttle sending messages (termed flow control). This trade-off decides between optimizing the throughput of a runtime scheduler by increasing the rate of execution of algorithmic work, and reducing the latency of the network messages. Another trade-off exists when a decision has to be made about when to send aggregated messages in buffers (message coalescing). This decision chooses between trading off latency for network bandwidth and vice versa. At any instant, such trade-offs emphasize either on improving the quantity of work being executed (by maximizing the scheduler throughput) or on improving the quality of work (by prioritizing better work). However, encoding static policies for different runtime features (such as flow control, coalescing) can prevent graph algorithms from achieving their full potentials, thus can under-mine the actual performance of a distributed graph algorithm . In this paper, we investigate runtime support for distributed graph algorithms in the context of two paradigms: variants of well-known Bulk-Synchronous Parallel model and asynchronous programming model. We explore generic runtime features such as message coalescing (aggregation) and flow control and show that execution policies of these features need to be adjusted over time to make a positive impact on the execution time of a distributed graph algorithm. Since synchronous and asynchronous graph algorithms have different workload characteristics, not all of such runtime features may be good candidates for adaptation. Each of these algorithmic paradigms may require different set of features to be adapted over time. We demonstrate which set of feature(s) can be useful in each case to achieve the right balance of work in the runtime layer. Existing implementation of different graph algorithms can benefit from adapting dynamic policies in the underlying runtime.

In this paper we explore scheduling and runtime system support for unordered distributed graph computations that rely on optimistic (speculative) execution. Performance of such algorithms is impacted by two competing trends: the higher degree of parallelism enabled by optimistic execution in turn requires substantial runtime support. To address the potentially high overhead and scheduling complexity introduced by the runtime, we investigate customizable scheduling policies that augment the scheduler of the underlying runtime to adapt it to a specific graph application. We present several implementations of Distributed Control (DC), a data-driven unordered approach with work prioritization and demonstrate that customizable scheduling policies result in the most efficient implementation, outperforming the well-known ?-stepping Single-Source Shortest Paths (SSSP) and Jones-Plassmann vertex-coloring algorithms. We apply two scheduling techniques, flow control and adaptive frequency of network progress, which allow application-level control over the balance of domain work and the runtime work. Experimental results show the benefit of such application-aware scheduling for irregular distributed graph algorithms.

We propose a distributed, unordered, label-correcting distance-1 vertex coloring algorithm, called Distributed Control (DC) coloring algorithm. DC eliminates the need for vertex-centric barriers and global synchronization for color refinement, relying only on atomic operations and local termination detection to update vertex color. We implement our DC coloring algorithm and the well-known Jones-Plassmann algorithm in the AM++ AMT runtime and compare their performance. We show that, with runtime support, the elimination of waiting time of vertex-centric barriers and investing this time for local ordering results in better execution time for power-law graphs with dense local subgraphs.

Unordered graph algorithms can offer efficient resource utilization that is advantageous for performance in distributed setting. Unordered execution allows for parallel computation without synchronization. In unordered algorithms, work is data-driven and can be performed in any order, refining the result as the algorithm progresses. Unfortunately, a sub-optimal work ordering may lead to more time spent on correcting the results than on useful work. On HPC systems, the issue is compounded by irregular nature of distributed graph algorithms which makes them sensitive to the whole software/hardware stack, collectively referred to as runtime. In this paper, we consider an example of such algorithms: Distributed Control (DC) single-source shortest paths (SSSP). DC relies on performance gains stemming from the inherent asynchrony of unordered algorithms while optimizing work ordering locally. We demonstrate that distributed runtime scheduling policy can prevent effective work ordering optimization. We show that lifting and delegating some scheduling decisions to the algorithm level can result in significantly better performance. We propose that this strategy can be useful for performance engineering.

A Hamiltonian path in a graph is a path involving all the vertices of the graph. In this paper, we revisit the famous Hamiltonian path problem and present new sufficient conditions for the existence of a Hamiltonian path in a graph.

Measurements for distributed algorithms, such as performance results, are usually reported using averages, similarly to prevailing practice in other areas of computer science. We argue that including standard deviations offers additional information and that the minimal burden of providing standard deviations is outweighed by the benefits. We propose a new way of reporting run time speedup that incorporates standard deviation and demonstrate its usefulness in terms of two distributed graph algorithms.

With the advent of the exascale era, new runtimes and algorithm design techniques need to be explored. In this paper, we investigate performance of three different single-source shortest path algorithms in two relatively recent asynchronous many-task runtime systems AMpp and HPX-5. We identify the underlying set of differential features for these runtimes, and we compare and contrast the performance of D-stepping algorithm, Distributed Control based algorithm, K-level Asynchronous algorithm in AMpp and in HPX-5, for which we also include chaotic implementation. We observe that specific runtime characteristics or lack thereoff and different graph inputs can impact the feasibility of an algorithmic approach.

Due to the ever increasing complexity of the modern supercomputers, performance analysis of irregular applications became an experimental endeavor. We show that runtime considerations are inseparable from algorithmic concerns in performance engineering of large-scale distributed graph algorithms, and we argue that the whole system stack, starting with the algorithm at the top down to low-level communication libraries must be considered.

DNA fragment assembly problem is one of the crucial challenges faced by computational biologists where, given a set of DNA fragments, we have to construct a complete DNA sequence from them. As it is an NP-hard problem, accurate DNA sequence is hard to find. Moreover, due to experimental limitations, the fragments considered for assembly are exposed to additional errors while reading the fragments. In such scenarios, meta-heuristic based algorithms can come in handy. We analyze the performance of two swarm intelligence based algorithms namely Artificial Bee Colony (ABC) algorithm and Queen Bee Evolution Based on Genetic Algorithm (QEGA) to solve the fragment assembly problem and report quite promising results. Our main focus is to design meta-heuristic based techniques to efficiently handle DNA fragment assembly problem for noisy and noiseless data.