Fast lists intersection with Bloom filter using graphics processing units

Zhang, Fan; Wu, Di; Ao, Naiyong; Wang, Gang; Liu, Xiaoguang; Liu, Jing

doi:10.1145/1982185.1982363

Cited by 9 publications

(3 citation statements)

References 8 publications

(5 reference statements)

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“…[43] designed an efficient parallel GPU-based method to solve the problem of intersecting two unsorted sets. [44] used a special data structure called bloom filter to parallelize the intersections on GPU. [45] implemented parallel lists intersection and index compression on GPU to speed up web search.…”

Section: Related Workmentioning

confidence: 99%

SpArch: Efficient Architecture for Sparse Matrix Multiplication

Zhang

Wang

Han

et al. 2020

2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)

191

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Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various engineering and scientific applications. However, inner product based SpGEMM introduces redundant input fetches for mismatched nonzero operands, while outer product based approach [1] suffers from poor output locality due to numerous partial product matrices. Inefficiency in the reuse of either inputs or outputs data leads to extensive and expensive DRAM access.To address this problem, this paper proposes an efficient sparse matrix multiplication accelerator architecture, SpArch, which jointly optimizes the data locality for both input and output matrices. We first design a highly parallelized streamingbased merger to pipeline the multiply and merge stage of partial matrices so that partial matrices are merged on chip immediately after produced. We then propose a condensed matrix representation that reduces the number of partial matrices by three orders of magnitude and thus reduces DRAM access by 5.4×. We further develop a Huffman tree scheduler to improve the scalability of the merger for larger sparse matrices, which reduces the DRAM access by another 1.8×. We also resolve the increased input matrix read induced by the new representation using a row prefetcher with near-optimal buffer replacement policy, further reducing the DRAM access by 1.5×. Evaluated on 20 benchmarks, SpArch reduces the total DRAM access by 2.8× over previous state-of-the-art. On average, SpArch achieves 4×, 19×, 18×, 17×, 1285× speedup and 6×, 164×, 435×, 307×, 62× energy savings over OuterSPACE, MKL, cuSPARSE, CUSP, and ARM Armadillo, respectively.

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Section: Related Workmentioning

confidence: 99%

SpArch: Efficient Architecture for Sparse Matrix Multiplication

Zhang

Wang

Han

et al. 2020

2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)

191

View full text Add to dashboard Cite

show abstract

“…For example, as the hash table approaches capacity, the increase in collisions can cause a lookup to probe through multiple candidates, sometimes hundreds, before finding an element doesn't exist. Bloom filters have been used to implement fast list intersection problems for sparse matrix multiplication problems on the GPU [54,55]. As an alternative to the hash table approach, we tried building a bloom filter in shared memory and used a binary search to perform lookups of nonzeros in global memory for positive hits.…”

Section: Load Balanced Hybrid Csr+coomentioning

confidence: 99%

GPU Semiring Primitives for Sparse Neighborhood Methods

Nolet¹,

Divye²,

Raff³

et al. 2021

Preprint

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High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations. We demonstrate that a sparse semiring primitive can be flexible enough to support a wide range of critical distance measures while maintaining performance and memory efficiency on the GPU. We further show that this primitive is a foundational component for enabling many neighborhood-based information retrieval and machine learning algorithms to accept sparse input. To our knowledge, this is the first work aiming to unify the computation of several critical distance measures on the GPU under a single flexible design paradigm and we hope that it provides a good baseline for future research in this area.

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“…Queries are grouped into batches which are processed by GPU threads in parallel. Later, the work presented by Zhang et al proposed new techniques for improving the performance of the GPU batched algorithm previously presented by Wu et al…”

Section: Background and Related Workmentioning

confidence: 99%

Performance evaluation of single vs. batch of queries on GPUs

Gaioso

Gil-Costa

Guardia

et al. 2019

Concurrency and Computation

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Summary The WAND processing strategy is a dynamic pruning algorithm designed for large scale Web search engines where fast response to queries is a critical service. The WAND is used to reduce the amount of computation by scoring only documents that may become part of the top‐k document results. In this paper, we present two parallel strategies for the WAND algorithm and compare their performance on GPUs. In our first strategy (named size‐based), the posting lists are evenly partitioned among thread blocks. Our second strategy (named range‐based) partitions the posting lists according to document identifier intervals; thus, partitions may have different sizes. We also propose three threshold sharing policies, named Local, Safe‐R, and Safe‐WR, which emulate the WAND algorithm global pruning technique. We evaluated our proposals with different amounts of work, from short to extra‐large queries, using single query processing and batch of queries. Results show that the size‐based strategy reports the highest speedups but at the cost of low quality of results. The range‐based algorithm retrievals the exact top‐k documents and maintains a good speedup. Moreover, both strategies are capable of scaling as the amount of work is increased. In addition, there is no significant difference in the performance of the three threshold sharing policies.

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Fast lists intersection with Bloom filter using graphics processing units

Cited by 9 publications

References 8 publications

SpArch: Efficient Architecture for Sparse Matrix Multiplication

SpArch: Efficient Architecture for Sparse Matrix Multiplication

GPU Semiring Primitives for Sparse Neighborhood Methods

Performance evaluation of single vs. batch of queries on GPUs

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