skip to main content


Title: Scalability of Sparse Matrix Dense Vector Multiply (SpMV) on a Migrating Thread Architecture
Sparse matrix dense vector multiplication (SpMV), exhibits the memory bandwidth and communication driven nature of many sparse linear algebra operations. Irregular memory accesses from the non-zero structure within a sparse matrix wreak havoc on performance. This paper presents strong scaling for communication avoiding SpMV implementations on a migrating thread system intended to address the lack of locality in sparse problems. We developed communication avoiding SpMV code to attempt to reduce off-node thread migration by using the hypergraph partitioning package HYPE to determine workload distribution. Additionally, we investigate the performance impact of overlapping communication and computation through the use of remote memory operations supported by the architecture. Incorporating remote memory operations with hypergraph partitioning we achieved 6.18X speedup for overall performance.  more » « less
Award ID(s):
1822939
NSF-PAR ID:
10199744
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Page Range / eLocation ID:
483 to 488
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Communication overhead has been identified as the primary factor in overall performance degradation for sparse and irregular problems such as SpMV. Many works have shown significant communication reductions, but only for matrices with specific characteristics and by dramatically reworking the computations. This study develops and evaluates a communication avoiding distributed heterogeneous implementation for strong scaling of SpMV on the Sierra supercomputer architecture. To address the far bigger matrices characteristic of real problems, we utilize a hypergraph partitioning package HYPE to determine workload distribution and reduce inter-node communication. Additionally we investigated the performance impact of performing hypergraph partitioning on scale free graphs which had undergone a vertex delegation pre-processing step. We achieved up to 97% reduction in average message size per process at scale when using the HYPE partitioner. Despite this we show how optimizing SpMV on existing GPU architectures does provide increased computational performance, yet does not address the dominant communication overhead factor at scale despite attempts to avoid communication where possible. 
    more » « less
  2. SpMV, the product of a sparse matrix and a dense vector, is emblematic of a new class of applications that are memory bandwidth and communication, not flop, driven. Sparsity and randomness in such computations play havoc with performance, especially when strong, instead of weak, scaling is attempted. In this study we develop and evaluate a hybrid implementation for strong scaling of the Compressed Vectorization-oriented sparse Row (CVR) approach to SpMV on a cluster of Intel Xeon Phi Knights Landing (KNL) processors. We show how our hybrid SpMV implementation achieves increased computational performance, yet does not address the dominant communication overhead factor at extreme scale. Issues with workload distribution, data placement, and remote reductions are assessed over a range of matrix characteristics. Our results indicate that as P 􀀀! 1 communication overhead is by far the dominant factor despite improved computational performance. 
    more » « less
  3. null (Ed.)
    SpMV, the product of a sparse matrix and a dense vector, is emblematic of a new class of applications that are memory bandwidth and communication, not flop, driven. Sparsity and randomness in such computations play havoc with performance, especially when strong, instead of weak, scaling is attempted. In this study we develop and evaluate a hybrid implementation for strong scaling of the Compressed Vectorization-oriented sparse Row (CVR) approach to SpMV on a cluster of Intel Xeon Phi Knights Landing (KNL) processors. We show how our hybrid SpMV implementation achieves increased computational performance, yet does not address the dominant communication overhead factor at extreme scale. Issues with workload distribution, data placement, and remote reductions are assessed over a range of matrix characteristics. Our results indicate that as P 􀀀! 1 communication overhead is by far the dominant factor despite improved computational performance. 
    more » « less
  4. Sparse matrices are very common types of information used in scientific and machine learning applications including deep neural networks. Sparse data representations lead to storage efficiencies by avoiding storing zero values. However, sparse representations incur metadata computational overheads – soft- ware first needs to find row/column locations of non-zero val- ues before performing necessary computations. Such metadata accesses involve indirect memory accesses (of the form a[b[i]] where a[.] and b[.] are large arrays) and they are cache and prefetch-unfriendly, resulting in frequent load stalls. In this paper, we will explore a dedicated hardware for a memory-side accelerator called Hardware Helper Thread (HHT) that performs all the necessary index computations to fetch only the nonzero elements from sparse matrix and sparse vector and supply those values to the primary core, creating heterogeneity within a single CPU core. We show both performance gains and energy savings of HHT for sparse matrix-dense vector multiplication (SpMV) and sparse matrix- sparse vector multiplication (SpMSpV). The ASIC HHT shows average performance gains ranging between 1.7 and 3.5 de- pending on the sparsity levels, vector-widths used by RISCV vector instructions and if the Vector (in Matrix-Vector multi- plication) is sparse or dense. We also show energy savings of 19% on average when ASIC HHT is used compared to baseline (for SpMV), and the HHT requires 38.9% of a RISCV core area 
    more » « less
  5. SpMV, the product of a sparse matrix and a dense vector, is emblematic of a new class of applications that are memory bandwidth and communication, not flop, driven. Sparsity and randomness in such computations play havoc with conventional implementations, especially when strong, instead of weak, scaling is attempted. This paper studies improved hybrid SpMV codes that have better performance, especially for the sparsest of such problems. Issues with both data placement and remote reductions are modeled over a range of matrix characteristics. Those factors that limit strong scalability are quantified. 
    more » « less