GraphBLAS C API: Ideas for future versions of the specification

Mattson, Timothy G.; Yang, Carl; McMillan, Scott; Buluç, Aydın; Moreira, José E.

doi:10.1109/hpec.2017.8091095

Cited by 20 publications

(8 citation statements)

References 4 publications

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“…A full treatment of Graph-BLAS is beyond the scope of this paper; we give a brief introduction to the reader, so that he or she can better follow our contributions in later sections. We refer the interested reader to the Graph-BLAS C API specification [17] and selected papers [18,50,57] for a full treatment. At the end of this section, we give a running example (Section 3.4).…”

Section: Graphblas Conceptsmentioning

confidence: 99%

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GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Yang

Buluç

Owens

2022

ACM Trans. Math. Softw.

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High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which allow graph algorithms to be expressed in a performant, succinct, composable, and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity , which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in “GraphBLAST”, the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.

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Section: Graphblas Conceptsmentioning

confidence: 99%

“…( 6) We have matrix-vector, matrix-scalar, and vector-scalar variants of elementwise addition and multiplication for convenience and performance. These variants are called rank promotion [57] or Numpy-style broadcasting [43].…”

Section: Differences With Graphblas C Api Standardmentioning

confidence: 99%

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Yang

Buluç

Owens

2022

ACM Trans. Math. Softw.

Self Cite

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“…Bit representations (bitmaps, bitvectors) have been used in vertex-based graph frameworks [14]- [16] for representing frontiers (i.e., active nodes); bit-level optimizations have, however, not yet been systematically explored in matrixbased graph frameworks. Existing GraphBLAS [17], [18] frameworks typically build on existing linear algebra libraries, which offer no bit-level representations of matrices or bit-level implementations of linear algebra functions.…”

Section: Introductionmentioning

confidence: 99%

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Chen¹,

Sung²,

Shen³

et al. 2022

Preprint

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In a general graph data structure like an adjacency matrix, when edges are homogeneous, the connectivity of two nodes can be sufficiently represented using a single bit. This insight has, however, not yet been adequately exploited by the existing matrix-centric graph processing frameworks. This work fills the void by systematically exploring the bit-level representation of graphs and the corresponding optimizations to the graph operations. It proposes a two-level representation named Bit-Block Compressed Sparse Row (B2SR) and presents a series of optimizations to the graph operations on B2SR by leveraging the intrinsics of modern GPUs. Evaluations on NVIDIA Pascal and Volta GPUs show that the optimizations bring up to 40× and 6555× for essential GraphBLAS kernels SpMV and SpGEMM, respectively, making GraphBLAS-based BFS accelerate up to 433×, SSSP, PR, and CC up to 35×, and TC up to 52×.

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“…General sparse matrix-matrix multiplication (SpGEMM) operation multiplies a sparse matrix A with a sparse matrix B and generates a resulting sparse matrix C. This is an essential building block in a number of applications such as algebraic multigrid methods [1], shortest path algorithms [2], breadth first search algorithms [3], and Markov cluster algorithms [4]. It is also an important kernel in the GraphBLAS standard [5,6]. As a result, fast algorithms for parallel SpGEMM received much more attention in recent years [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Register-Aware Optimizations for Parallel Sparse Matrix–Matrix Multiplication

Liu

et al. 2019

Int J Parallel Prog

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General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block of a number of high-level algorithms and real-world applications. In recent years, several efficient SpGEMM algorithms have been proposed for many-core processors such as GPUs. However, their implementations of sparse accumulators, the core component of SpGEMM, mostly use low speed on-chip shared memory and global memory, and high speed registers are seriously underutilised. In this paper, we propose three novel register-aware SpGEMM algorithms for three representative sparse accumulators, i.e., sort, merge and hash, respectively. We fully utilise the GPU registers to fetch data, finish computations and store results out. In the experiments, our algorithms deliver excellent performance on a benchmark suite including 205 sparse matrices from the SuiteSparse Matrix Collection. Specifically, on an Nvidia Pascal P100 GPU, our three register-aware sparse accumulators achieve on average 2.0x (up to 5.4x), 2.6x (up to 10.5x) and 1.7x (up to 5.2x) speedups over their original implementations in libraries bhSPARSE, RMerge and NSPARSE, respectively.

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GraphBLAS C API: Ideas for future versions of the specification

Cited by 20 publications

References 4 publications

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Register-Aware Optimizations for Parallel Sparse Matrix–Matrix Multiplication

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