The Scalable Heterogeneous Computing (SHOC) benchmark suite

By Anthony Danalis, G. Marin, Collin McCurdy, J. Meredith, P. Roth, Kyle Spafford, V. Tipparaju, Jeffrey S. Vetter. GPGPU-3, 2010

717 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems

View Paper: https://www.semanticscholar.org/paper/00156e79606084497789662dfaf59c3b54a10722

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Citations in the corpus

Estimation of energy consumption in machine learning (2019, methodology): “Finally, they validate their model using the SHOC benchmark suite [16] and real measurements from their Xeoh Phi card, with 60 cores and four threads.”
Accel-Sim: An Extensible Simulation Framework for Validated GPU Modeling (2018, methodology): “discriminately include all the workloads we could get to run in simulation from the original GPGPU-Sim paper [8], Rodinia 3.1 [22], the CUDA SDK [23], Parboil [24], Pannotia [25], Lonestar [26], SHOC [27], and a collection of DOE proxy apps [28]. The sum total of which is over 1400 unique kernel lau”
MachSuite: Benchmarks for accelerator design and customized architectures (2014, methodology): “MachSuite contains a basic, 9-point, 2D stencil [18].”
A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads (2010, background): “Parboil [30] and SHOC [12] are two efforts to benchmark GPUs, but the former does not provide any diversity analysis and the latter targets systems with multiple GPU nodes.”
End-to-End Deep Learning of Optimization Heuristics (2017, neutral): “0 14 27 SHOC [34] 1.”
MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency (2018, methodology): “We randomly select 27 applications from the CUDA SDK [94], Rodinia [31], Parboil [121], LULESH [65, 66], and SHOC [37] suites.”
A Comprehensive Performance Comparison of CUDA and OpenCL (2011, background): “Anthony Danalis et al. [21] presented a Scalable HeterOgeneous Computing (SHOC) benchmark suite.”
A Survey of CPU-GPU Heterogeneous Computing Techniques (2015, neutral): “SHOC (Scalable HeterOgeneous Computing) [Danalis et al. 2010] provides both low-level microbenchmarks (to evaluate architectural features of the system) and applica-ACM [Agullo et al. 2011; ´Alvarez-Melc´on et al. 2013; Anzt et al. 2011; Becchi et al. 2010; Benner et al. 2011, 2010; Bernab´e et al.…”
Neural Code Comprehension: A Learnable Representation of Code Semantics (2018, neutral): “Lines Size Pairs Machine Learning Tensorflow [1] 2,492 16,943,893 220,554 260,250,973 High-Performance Computing AMD APP SDK [9] 123 1,304,669 4,146 45,081,359 BLAS [22] 300 280,782 566 283,856 Benchmarks NAS [57] 268 572,521 1,793 1,701,968 Parboil [59] 151 118,575 2,175 151,916 PolybenchGPU [27] 4”