high performance computing on graphics processing units: hgpu.org

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Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

Hector Dearman

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Tags: Benchmarking, Computer science, CUDA, Differential equations, FEM, Finite element method, nVidia, nVidia GRID K520, OpenCL, Partial differential equations, PDEs, Thesis

November 3, 2015 by hgpu

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K40

October 22, 2015 by hgpu

Comparison of Thread Execution Methods for GPU-oriented OpenCL Programs on Multicore Processors

Naohisa Hojo, Ittetsu Taniguchi, Hiroyuki Tomiyama

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Tags: Benchmarking, Computer science, OpenCL, Performance

October 16, 2015 by hgpu

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Felix Weninger, Johannes Bergmann, Bjorn Schuller

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 560, Package, Speech recognition

October 8, 2015 by hgpu

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Rahul Garg

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Tags: AMD Radeon HD 8650, ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Computer science, Intel HD Graphics 4000, Matlab, Matrix multiplication, nVidia, nVidia GeForce GT 650 M, OpenCL, Package, Python, Tesla C2050, Thesis

October 6, 2015 by hgpu

A Design Framework for Mapping Dataflow Graphs onto Heterogeneous Multiprocessor Platforms

Shuoxin Lin

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Tags: Benchmarking, Computer science, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, Thesis

September 29, 2015 by hgpu

Adaptive and Transparent Cache Bypassing for GPUs

Ang Li, Gert-Jan van den Braak, Akash Kumar, Henk Corporaal

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Tags: Benchmarking, cache, Computer science, Memory, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 570, nVidia GeForce GTX 690, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 980, Performance, PTX, Tesla K40, Tesla K80

September 24, 2015 by hgpu

Scalable Metropolis Monte Carlo for simulation of hard shapes

Joshua A. Anderson, M. Eric Irrgang, Sharon C. Glotzer

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Tags: Benchmarking, Chemistry, Condensed matter, CUDA, Monte Carlo simulation, nVidia, Physics, Soft Condensed Matter, Tesla K20

September 17, 2015 by hgpu

linalg: Matrix Computations in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Sparks, Alexander Ulanov, Matei Zaharia

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Scala, Tesla M2050

September 15, 2015 by hgpu

A Performance Comparison of Algebraic Multigrid Preconditioners on CPUs, GPUs, and Xeon Phis

K. Rupp, J. Weinbub, F. Rudolf, A. Morhammer, T. Grasser, A. Jungel

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Tags: AMD FirePro W9100, ATI, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, OpenMP, Performance, Tesla K20

September 9, 2015 by hgpu

Dissecting GPU Memory Hierarchy through Microbenchmarking

Xinxin Mei, Xiaowen Chu

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Tags: Benchmarking, cache, Computer science, CUDA, Hardware Architecture, nVidia, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Package

September 9, 2015 by hgpu

Partitioning Large Scale Deep Belief Networks Using Dropout

Yanping Huang, Sai Zhang

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Tags: Benchmarking, Databases, Deep learning, Machine learning, nVidia, nVidia GeForce 8800 GS, Python, Statistics

August 31, 2015 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

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Comparison of Thread Execution Methods for GPU-oriented OpenCL Programs on Multicore Processors

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

A Design Framework for Mapping Dataflow Graphs onto Heterogeneous Multiprocessor Platforms

Adaptive and Transparent Cache Bypassing for GPUs

Scalable Metropolis Monte Carlo for simulation of hard shapes

linalg: Matrix Computations in Apache Spark

A Performance Comparison of Algebraic Multigrid Preconditioners on CPUs, GPUs, and Xeon Phis

Dissecting GPU Memory Hierarchy through Microbenchmarking

Partitioning Large Scale Deep Belief Networks Using Dropout

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

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