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GPU Computing with Python: Performance, Energy Efficiency and Usability

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 840 M, OpenCL, Performance, Python, Tesla K20, Tesla M2090, Tesla P100, Tesla V100

December 8, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

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Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hamid Reza Zohouri, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

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

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Nico Reissmann

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Tags: Computer science, CUDA, nVidia, OpenMP, Package, Performance, performance portability, Tesla K20, Thesis

May 5, 2019 by hgpu

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Heinrich Riebler, Gavin Vaz, Tobias Kenter, Christian Plessl

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Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

May 1, 2019 by hgpu

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Tomas Oehberg, August Ernstsson, Christoph Kessler

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, MapReduce, nVidia, OpenCL, Package, Tesla K20

March 31, 2019 by hgpu

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Ada Sedova, Andreas Tillack, Arnold Tharrington

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Tags: Chemistry, Molecular dynamics, Molecular simulation, nVidia, OpenACC, performance portability, Physics, Tesla K20

March 3, 2019 by hgpu

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

B. P'erez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguad'e

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Tags: Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, Tesla K20

January 13, 2019 by hgpu

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

Samuel Thibault

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Operating systems, StarPU, Task scheduling, Tesla C2050, Tesla K20, Tesla M2075, Thesis

December 23, 2018 by hgpu

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Hang Liu, H. Howie Huang

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Tags: Computer science, CUDA, Graph theory, nVidia, Tesla K20, Tesla K40, Tesla P100

December 16, 2018 by hgpu

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Fumiya Kono

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Tags: AMD FirePro W8100, AMD FirePro W9100, AMD Radeon R9 280X, ATI, CUDA, Earth and Space Sciences, FPGA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Tesla K20, Thesis

December 9, 2018 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

GPU Computing with Python: Performance, Energy Efficiency and Usability

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

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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