high performance computing on graphics processing units: hgpu.org

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Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Dominik Grewe, Anton Lokhmotov

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Tags: ATI, ATI Radeon HD 5970, Code generation, Computer science, CUDA, High-level Languages, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla S1070

April 21, 2011 by hgpu

Assessment of GPU computational enhancement to a 2D flood model

Alfred J. Kalyanapu, Siddharth Shankar, Eric R. Pardyjak, David R. Judi, Steven J. Burian

Tags: CUDA, Earth and Space Sciences, Fluid simulation, nVidia

April 21, 2011 by hgpu

Solving knapsack problems on GPU

V. Boyer, D. El Baz, M. Elkihel

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Optimization

April 21, 2011 by hgpu

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 9500 GTX, nVidia GeForce GTX 295, Sparse matrix

April 21, 2011 by hgpu

A performance prediction model for the CUDA GPGPU platform

Kishore Kothapalli, Rishabh Mukherjee, M. Suhail Rehman, Suryakant Patidar, P. J. Narayanan, Kannan Srinathan

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Performance

April 21, 2011 by hgpu

Fine-sorting One-dimensional Particle-In-Cell Algorithm with Monte-Carlo Collisions on a Graphics Processing Unit

Philipp Mertmann, Denis Eremin, Thomas Mussenbrock, Ralf Peter Brinkmann, Peter Awakowicz

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 480, Particle-in-cell methods, Physics, Plasma physics, Sorting

April 21, 2011 by hgpu

Exploring scalability of FIR filter realizations on Graphics Processing Units

J. Rebacz, E. Oruklu, J. Saniie

Tags: CUDA, Filtering, nVidia, Signal processing

April 20, 2011 by hgpu

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis

T. Nagaoka, S. Watanabe

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

April 20, 2011 by hgpu

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Wang Lei, Zhang Yunquan, Zhang Xianyi, Liu Fangfang

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia GeForce GTX 295, Tesla C1060

April 20, 2011 by hgpu

Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?

Eric S. Chung, Peter A. Milder, James C. Hoe, Ken Mai

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Tags: ASIC, ATI, ATI CAL, ATI Radeon HD 5870, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance

April 19, 2011 by hgpu

A Hybrid Analytical DRAM Performance Model

George L. Yuan, Tor M. Aamodt

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Tags: Computer science, CUDA, Hardware Architecture, Memory, nVidia

April 19, 2011 by hgpu

Dynamic warp formation: Efficient MIMD control flow on SIMD graphics hardware

Wilson W. L. Fung, Ivan Sham, George Yuan, Tor M. Aamodt

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Tags: Computer science, CUDA, Hardware Architecture, nVidia, nVidia GeForce 8800 GTX, Performance, Warp level parallelism

April 19, 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

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Assessment of GPU computational enhancement to a 2D flood model

Solving knapsack problems on GPU

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

A performance prediction model for the CUDA GPGPU platform

Fine-sorting One-dimensional Particle-In-Cell Algorithm with Monte-Carlo Collisions on a Graphics Processing Unit

Exploring scalability of FIR filter realizations on Graphics Processing Units

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs?

A Hybrid Analytical DRAM Performance Model

Dynamic warp formation: Efficient MIMD control flow on SIMD graphics hardware

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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