Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

By John A. Stratton, Christopher I. Rodrigues, I-Jui Sung, N. Obeid, Li-Wen Chang, N. Anssari, Geng Liu, Wen-mei W. Hwu. 2012

848 citations recorded by the source corpus.

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Citations in the corpus

Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations (2018, methodology): “We used perf with a sampling rate of 2 seconds to profile 15 cryptocurrency mining applications and 19 non-mining applications from the Rodinia [9] and Parboil [40] benchmark suites.”
Mining on Someone Else's Dime: Mitigating Covert Mining Operations in Clouds and Enterprises (2017, methodology): “Figure 2 presents this comparison between mining software and some popular and common GPU workloads taken from the Rodinia [33] and Parboil [49] GPU-benchmark suites.”
A quantitative study of irregular programs on GPUs (2012, background): “present the Parboil benchmark suite for throughput-computing architectures and compilers [43].”
MachSuite: Benchmarks for accelerator design and customized architectures (2014, background): “Many high-quality benchmarks have emerged from the GPGPU community in the last several years [9], [18], [44].”
Accel-Sim: An Extensible Simulation Framework for Validated GPU Modeling (2018, methodology): “With the exception of a handful of applications with trace-size issues, we run all the applications found in Rodinia 3.1 [9], the CUDA SDK [40], Parboil [65], Polybench [16], CUTLASS [47], our microbenchmark suite, and cutting-edge machine learning workloads from the Deepbench benchmark suite [35].”
A Survey of CPU-GPU Heterogeneous Computing Techniques (2015, unknown): “For the same fundamental algorithm, Parboil [Stratton et al. 2012] provides versions of varying levels of optimizations.”
Zorua: A holistic approach to resource virtualization in GPUs (2016, methodology): “resulting utilization of physical resources on GPU performance, we conduct an experiment on a Maxwell GPU system (GTX 745) with 20 GPGPU workloads from the CUDA SDK [52], Rodinia [9], GPGPU-Sim benchmarks [5], Lonestar [8], Parboil [66], and US DoE application suites [72].”
NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules (2015, methodology): “[49] J. Stratton et al., “Parboil: A revised benchmark suite for scientific and commercial throughput computing,” 2012.”
Neural Code Comprehension: A Learnable Representation of Code Semantics (2018, unknown): “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”
OWL: cooperative thread array aware scheduling techniques for improving GPGPU performance (2013, methodology): “SPMV H (2.19x) 65% 30 Rodinia Leukocyte LYTE L (1.08x) 15% 12 3rd Party JPEG Decoding JPEG H (2.12x) 54% 31 Rodinia LU Decomposition LUD L (1.05x) 64% 13 Rodinia Breadth First Search BFSR H (2.09x) 64% 32 Parboil Matrix Multiplication MM L (1.04x) 4% 14 Rodinia Streamcluster SC H (1.94x) 52% 33 C”
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.”