high performance computing on graphics processing units: hgpu.org

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A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Michael J. Anderson, David Sheffield, Kurt Keutzer

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Quadro FX 6000, Package, Signal processing

December 16, 2011 by hgpu

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Uzi Vishkin, Rajeev Barua

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Tags: Algorithms, Benchmarking, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Prefetch, Rendering, Thesis

December 16, 2011 by hgpu

Affine Vector Cache for memory bandwidth savings

Sylvain Collange, Alexandre Kouyoumdjian

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Tags: Benchmarking, Computer science, CUDA, Memory model, nVidia, Tesla

December 16, 2011 by hgpu

Memory-level and Thread-level Parallelism Aware GPU Architecture Performance Analytical Model

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Benchmarking, Computer science, CUDA, Memory level parallelism, nVidia, nVidia Quadro FX 5600, Warp level parallelism

December 15, 2011 by hgpu

Performance in GPU Architectures: Potentials and Distances

Ahmad Lashgar, Amirali Baniasadi

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5800, Performance

December 15, 2011 by hgpu

Graph Generation on GPUs using Dynamic Memory Allocation

A. Leist, K.A. Hawick

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Graph theory, Memory model, nVidia, nVidia GeForce GTX 580, Performance

December 14, 2011 by hgpu

Fast Neural Network Training on General Purpose Computers

Harshit Kharbanda, Roy H. Campbell

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia Quadro FX 1800

December 14, 2011 by hgpu

GPU Programming in a High Level Language: Compiling X10 to CUDA

Dave Cunningham, Rajesh Bordawekar, Vijay Saraswat

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia Quadro FX 3700 M, OpenCL, Tesla C1060, Tesla S1070

December 12, 2011 by hgpu

Multi-Science Applications with Single Codebase – GAMER – for Massively Parallel Architectures

Hemant Shukla, Hsi-Yu Schive, Tak-Pong Woo, Tzihong Chiueh

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Tags: Algorithms, Astrophysics, Benchmarking, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Physics, Rendering, Tesla C2050

December 10, 2011 by hgpu

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Deepak Mathews Panickal

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Tags: ATI, ATI Radeon HD 5470, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Tesla C2070, Thesis

December 9, 2011 by hgpu

Towards a complete FEM-based simulation toolkit on GPUs: Geometric Multigrid solvers

Markus Geveler, Dirk Ribbrock, Dominik Goddeke, Peter Zajac, Stefan Turek

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Tags: Benchmarking, CUDA, Differential equations, FEM, Finite element method, Fluid dynamics, nVidia, nVidia GeForce GTX 285, Partial differential equations, PDEs, Sparse matrix

December 7, 2011 by hgpu

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Alecio Pedro Delazari Binotto

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, OpenCL, Task scheduling, Thesis

December 5, 2011 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

See all packages

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high performance computing on graphics processing units: hgpu.org

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Affine Vector Cache for memory bandwidth savings

Memory-level and Thread-level Parallelism Aware GPU Architecture Performance Analytical Model

Performance in GPU Architectures: Potentials and Distances

Graph Generation on GPUs using Dynamic Memory Allocation

Fast Neural Network Training on General Purpose Computers

GPU Programming in a High Level Language: Compiling X10 to CUDA

Multi-Science Applications with Single Codebase – GAMER – for Massively Parallel Architectures

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Towards a complete FEM-based simulation toolkit on GPUs: Geometric Multigrid solvers

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Recent source codes

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

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