high performance computing on graphics processing units: hgpu.org

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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

A Unified Approach to Variable Renaming for Enhanced Vectorization

Prasanth Chatarasi, Jun Shirako, Albert Cohen, Vivek Sarkar

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Tags: Benchmarking, Compilers, Computer science, CUDA, Intel Xeon Phi, nVidia, Tesla V100

May 15, 2019 by hgpu

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Dominik Ernst, Georg Hager, Jonas Thies, Gerhard Wellein

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Performance, Tesla V100

May 12, 2019 by hgpu

Evaluating the Arm Ecosystem for High Performance Computing

Adrian Jackson, Andrew Turner, Michele Weiland, Nick Johnson, Olly Perks, Mark Parsons

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Tags: ARM, Benchmarking, Computer science, Distributed computing, HPC, Performance

May 1, 2019 by hgpu

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Zhe Jia, Marco Maggioni, Jeffrey Smith, Daniele Paolo Scarpazza

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, Performance, PTX, Tesla P100, Tesla V100

March 24, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 2019 by hgpu

Exploring FPGA-specific Optimizations for Irregular OpenCL Applications

Mohamed W. Hassan, Ahmed E. Helal, Peter M. Athanas, Wu-Chun Feng, Yasser Y. Hanafy

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Tags: Benchmarking, Computer science, FPGA, HLS, Linear Algebra, OpenCL, Package

January 20, 2019 by hgpu

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Aajna Karki, Chethan Palangotu Keshava, Spoorthi Mysore Shivakumar, Joshua Skow, Goutam Madhukeshwar Hegde, Hyeran Jeon

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, OpenCL, Package

January 20, 2019 by hgpu

cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

Behrooz Zare, Foad Jafarinejad, Matin Hashemi, Saber Salehkaleybar

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Tags: Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080

December 23, 2018 by hgpu

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

Patrick Lavin, Jason Riedy, Rich Vuduc, Jeffrey Young

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Tags: Benchmarking, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Performance, Tesla P100

November 18, 2018 by hgpu

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Xu Liu, Hibat Allah Ounifi, Abdelouahed Gherbi, Yves Lemieux, Wubin Li

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Tags: Algorithms, Benchmarking, CNN, Computer science, FPGA, Hybrid computing, Machine learning, nVidia, nVidia GeForce GTX Titan XP, OpenCL

November 11, 2018 by hgpu

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc Van Gool

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Tags: Android, Artificial intelligence, Benchmarking, Computer science, Deep learning, Neural networks

October 21, 2018 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Instructions’ Latencies Characterization for NVIDIA GPGPUs

A Unified Approach to Variable Renaming for Enhanced Vectorization

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Evaluating the Arm Ecosystem for High Performance Computing

Dissecting the NVidia Turing T4 GPU via Microbenchmarking

TensorFlow Doing HPC

Exploring FPGA-specific Optimizations for Irregular OpenCL Applications

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)