high performance computing on graphics processing units: hgpu.org

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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

GROPHECY: GPU performance projection from CPU code skeletons

Jiayuan Meng, Vitali A. Morozov, Kalyan Kumaran, Venkatram Vishwanath, Thomas D. Uram

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Tags: Analytical model, Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, Performance, Tesla C1060

December 5, 2011 by hgpu

GPU-Based Parallel Multi-objective Particle Swarm Optimization

You Zhou, Ying Tan

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, Optimization

December 4, 2011 by hgpu

Comparison of CPML Implementations for the GPU-Accelerated FDTD Solver

J.I. Toivanen, T.P. Stefanski, N. Kuster, N. Chavannes

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Tags: Benchmarking, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, nVidia, Tesla C1060

December 3, 2011 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

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

GROPHECY: GPU performance projection from CPU code skeletons

GPU-Based Parallel Multi-objective Particle Swarm Optimization

Comparison of CPML Implementations for the GPU-Accelerated FDTD Solver

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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