GraphGrind

Sun, Jiawen; Vandierendonck, Hans; Nikolopoulos, Dimitrios S.

doi:10.1145/3079079.3079097

Cited by 56 publications

(3 citation statements)

References 27 publications

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“…In this case, chunk sizes can be chosen at compile time such that the total number of elements per chunk is balanced, as opposed to balancing the number of rows per chunk. Similar issues occur also in graph processing, where partitioning techniques are essential to achieve load balance regardless of scheduling policy 21 …”

Section: Scheduling Fine‐grain Parallel Loopsmentioning

confidence: 91%

Reducing the burden of parallel loop schedulers for many‐core processors

Arif

Vandierendonck

2021

Concurrency and Computation

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Summary As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP and Cilkplus parallel task schedulers to build hybrid schedulers. Compiler support enables efficient reductions for Cilk, without changing the programming interface of Cilk reducers. Detailed, quantitative measurements demonstrate that our techniques achieve scalable performance on a 48‐core machine and the scheduling overhead is 43% lower than Intel OpenMP and 12.1× lower than Cilk. We demonstrate consistent performance improvements on a range of HPC and data analytics codes. Performance gains are more important as loops become finer‐grain and thread counts increase. We observe consistently 16%–30% speedup on 48 threads, with a peak of 2.8× speedup.

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Section: Scheduling Fine‐grain Parallel Loopsmentioning

confidence: 91%

Reducing the burden of parallel loop schedulers for many‐core processors

Arif

Vandierendonck

2021

Concurrency and Computation

Self Cite

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“…Based on that, they proposed Polymer, a framework that optimizes the graph algorithm computations, enhancing data locality on real‐world input graphs. Sun et al 53 had investigated the adverse effects introduced by NUMA‐based graph partitions on the performance of graph analytics. Based on that, Sun et al 53 proposed GraphGrind, a NUMA‐aware graph analytics framework that addresses the limitations incurred by graph partitioning by providing a fair graph partition strategy and changing from different graph representations (e.g., Compressed Sparse Row and Compressed Sparse Colum) during the application execution.…”

Section: Related Workmentioning

confidence: 99%

Improving the efficiency of graph algorithm executions on high‐performance computing

Moori

Rocha

Schwarzrock

et al. 2022

Concurrency and Computation

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The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real-world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non-functional requirements. Based on that, we propose GraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy.GraphKat works in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to 434.26×) and energy-saving (up to 245.21×), and reduces the graph applications' execution time (up to 17.70×) and energy consumption (up to 6.64×) compared to the default execution of parallel applications on HPC systems.

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“…Graph Partitioning. Several (hyper-)graph-partitioning approaches have been proposed for sparse matrix-vector [8,12,37,46] and sparse matrix-matrix [6,19] multiplication. Our matrix decomposition approach is not based on graph partitioning.…”

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confidence: 99%

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication

Gianinazzi,

Ziogas,

Huang

et al. 2024

Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

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We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix into a small number of highly structured matrices called arrow matrices, which are connected by permutations. Our approach enables communication-avoiding multiplications, achieving a polynomial reduction in communication volume per iteration for matrices corresponding to planar graphs and other minor-excluded families of graphs. Our evaluation demonstrates that our approach outperforms a state-of-the-art method for sparse matrix multiplication on matrices with hundreds of millions of rows, offering nearlinear strong and weak scaling.

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GraphGrind

Cited by 56 publications

References 27 publications

Reducing the burden of parallel loop schedulers for many‐core processors

Reducing the burden of parallel loop schedulers for many‐core processors

Improving the efficiency of graph algorithm executions on high‐performance computing

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication

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