3,833 research outputs found
Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes
The ongoing hardware evolution exhibits an escalation in the number, as well
as in the heterogeneity, of computing resources. The pressure to maintain
reasonable levels of performance and portability forces application developers
to leave the traditional programming paradigms and explore alternative
solutions. PaStiX is a parallel sparse direct solver, based on a dynamic
scheduler for modern hierarchical manycore architectures. In this paper, we
study the benefits and limits of replacing the highly specialized internal
scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and
StarPU. The tasks graph of the factorization step is made available to the two
runtimes, providing them the opportunity to process and optimize its traversal
in order to maximize the algorithm efficiency for the targeted hardware
platform. A comparative study of the performance of the PaStiX solver on top of
its native internal scheduler, PaRSEC, and StarPU frameworks, on different
execution environments, is performed. The analysis highlights that these
generic task-based runtimes achieve comparable results to the
application-optimized embedded scheduler on homogeneous platforms. Furthermore,
they are able to significantly speed up the solver on heterogeneous
environments by taking advantage of the accelerators while hiding the
complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014
Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors
Sparse matrix-vector multiplication (SpMV) is a central building block for
scientific software and graph applications. Recently, heterogeneous processors
composed of different types of cores attracted much attention because of their
flexible core configuration and high energy efficiency. In this paper, we
propose a compressed sparse row (CSR) format based SpMV algorithm utilizing
both types of cores in a CPU-GPU heterogeneous processor. We first
speculatively execute segmented sum operations on the GPU part of a
heterogeneous processor and generate a possibly incorrect results. Then the CPU
part of the same chip is triggered to re-arrange the predicted partial sums for
a correct resulting vector. On three heterogeneous processors from Intel, AMD
and nVidia, using 20 sparse matrices as a benchmark suite, the experimental
results show that our method obtains significant performance improvement over
the best existing CSR-based SpMV algorithms. The source code of this work is
downloadable at https://github.com/bhSPARSE/Benchmark_SpMV_using_CSRComment: 22 pages, 8 figures, Published at Parallel Computing (PARCO
A Library for Pattern-based Sparse Matrix Vector Multiply
Pattern-based Representation (PBR) is a novel approach to improving the performance of Sparse Matrix-Vector Multiply (SMVM) numerical kernels. Motivated by our observation that many matrices can be divided into blocks that share a small number of distinct patterns, we generate custom multiplication kernels for frequently recurring block patterns.
The resulting reduction in index overhead significantly reduces memory bandwidth requirements and improves performance. Unlike existing methods, PBR requires neither detection of dense blocks nor zero filling, making it particularly advantageous for matrices that lack dense nonzero concentrations. SMVM kernels for PBR can benefit from explicit prefetching and vectorization, and are amenable to parallelization. The analysis and format conversion to PBR is implemented as a library, making it suitable for applications that generate matrices dynamically at runtime. We present sequential and parallel performance results for PBR on two current multicore architectures, which show that PBR outperforms available alternatives for the matrices to which it is applicable,
and that the analysis and conversion overhead is amortized in realistic application scenarios
Architecture-Aware Configuration and Scheduling of Matrix Multiplication on Asymmetric Multicore Processors
Asymmetric multicore processors (AMPs) have recently emerged as an appealing
technology for severely energy-constrained environments, especially in mobile
appliances where heterogeneity in applications is mainstream. In addition,
given the growing interest for low-power high performance computing, this type
of architectures is also being investigated as a means to improve the
throughput-per-Watt of complex scientific applications.
In this paper, we design and embed several architecture-aware optimizations
into a multi-threaded general matrix multiplication (gemm), a key operation of
the BLAS, in order to obtain a high performance implementation for ARM
big.LITTLE AMPs. Our solution is based on the reference implementation of gemm
in the BLIS library, and integrates a cache-aware configuration as well as
asymmetric--static and dynamic scheduling strategies that carefully tune and
distribute the operation's micro-kernels among the big and LITTLE cores of the
target processor. The experimental results on a Samsung Exynos 5422, a
system-on-chip with ARM Cortex-A15 and Cortex-A7 clusters that implements the
big.LITTLE model, expose that our cache-aware versions of gemm with asymmetric
scheduling attain important gains in performance with respect to its
architecture-oblivious counterparts while exploiting all the resources of the
AMP to deliver considerable energy efficiency
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