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Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Beau Johnston

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Tags: Algorithms, AMD Radeon RX 480, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K40, Thesis

September 15, 2019 by hgpu

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Abhinav Muta, Prabhu Ramachandran, Pawan Negi

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Tags: Benchmarking, Computational Physics, Fluid dynamics, Laplace and Poisson equation, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1070 Ti, OpenCL, Package, Poisson equation

August 11, 2019 by hgpu

Performance Comparison for Neuroscience Application Benchmarks

Andreas Herten, Thorsten Hater, Wouter Klijn, Dirk Pleiter

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Tags: Benchmarking, Computer science, CUDA, Neuroscience, nVidia, Performance, Tesla P100, Tesla V100

August 11, 2019 by hgpu

Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

Thomas Faict, Erik H. D'Hollander, Bart Goossens

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Tags: Algorithms, Benchmarking, Computer science, FPGA, Heterogeneous systems, OpenCL

August 5, 2019 by hgpu

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

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Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 by hgpu

WCCV: Improving the Vectorization of IF-statements with Warp-Coherent Conditions

Huihui Sun, Florian Fey, Jie Zhao, Sergei Gorlatch

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Tags: Benchmarking, Code generation, Computer science, LLVM, OpenCL, Package, Performance

June 30, 2019 by hgpu

Mirovia: A Benchmarking Suite for Modern Heterogeneous Computing

Bodun Hu, Christopher J. Rossbach

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, Package, Performance, Tesla P100

June 27, 2019 by hgpu

Massively Parallel GPU Memory Compaction

Matthias Springer, Hidehiko Masuhara

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan XP, Performance

May 30, 2019 by hgpu

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones

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Tags: ARM, Benchmarking, Computer science, Computer vision, CUDA, FPGA, nVidia, nVidia Jetson TX2, OpenCV, Package

May 26, 2019 by hgpu

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Yehia Arafa, Abdel-Hameed Badawy, Gopinath Chennupati, Nandakishore Santhi, Stephan Eidenbenz

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia Titan RTX, Performance, PTX, Tesla K40, Tesla P100, Tesla V100

May 23, 2019 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Demystifying the MLPerf Benchmark Suite

Survey and Benchmarking of Machine Learning Accelerators

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Performance Comparison for Neuroscience Application Benchmarks

Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

WCCV: Improving the Vectorization of IF-statements with Warp-Coherent Conditions

Mirovia: A Benchmarking Suite for Modern Heterogeneous Computing

Massively Parallel GPU Memory Compaction

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

Instructions’ Latencies Characterization for NVIDIA GPGPUs

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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