high performance computing on graphics processing units: hgpu.org

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StoreGPU: exploiting graphics processing units to accelerate distributed storage systems

Samer Al-Kiswany, Abdullah Gharaibeh, Elizeu Santos-Neto, George Yuan, Matei Ripeanu

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 9800 GTX, Package, Storage system

November 6, 2010 by hgpu

Benchmarking GPUs to tune dense linear algebra

Vasily Volkov, James W. Demmel

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GTX, nVidia GeForce GTX 280

November 4, 2010 by hgpu

On sorting and load balancing on GPUs

Daniel Cederman, Philippas Tsigas

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Sorting

November 2, 2010 by hgpu

Analyzing CUDA workloads using a detailed GPU simulator

Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong, Tor M. Aamodt

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, PTX

October 28, 2010 by hgpu

On GPU’s viability as a middleware accelerator

Samer A. Kiswany, Abdullah Gharaibeh, Elizeu S. Neto, Matei Ripeanu

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Package, Storage system

October 27, 2010 by hgpu

GPU-Quicksort: A practical Quicksort algorithm for graphics processors

Daniel Cederman, Philippas Tsigas

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Package, Sorting

October 27, 2010 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

StoreGPU: exploiting graphics processing units to accelerate distributed storage systems

Benchmarking GPUs to tune dense linear algebra

On sorting and load balancing on GPUs

Analyzing CUDA workloads using a detailed GPU simulator

On GPU’s viability as a middleware accelerator

GPU-Quicksort: A practical Quicksort algorithm for graphics processors

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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