high performance computing on graphics processing units: hgpu.org

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CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Weifeng Liu, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 290X, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Performance, Sparse matrix

March 20, 2015 by hgpu

maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Andrew Lavin

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Package

January 28, 2015 by hgpu

Performance Analysis of Join Algorithms on GPUs

Ran Rui, Hao Li, Yi-Cheng Tu

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Tags: Algorithms, Computer science, CUDA, Databases, Join, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, Performance

January 26, 2015 by hgpu

Dynamic Feature-Adaptive Subdivision

H. Schafer, J. Raab, B. Keinert, M. Meyer, M. Stamminger, M. Niessner

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Tags: Computational Geometry, Computer science, DirectX, Interactive application, nVidia, nVidia GeForce GTX 980, Rendering

January 10, 2015 by hgpu

Code Optimization on Kepler GPUs and Xeon Phi

Yong-Chull Jang, Hwancheol Jeong, Jangho Kim, Weonjong Lee, Jeonghwan Pak, Yuree Chung (SWME Collaboration)

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Tags: CUDA, High Energy Physics - Lattice, Intel Xeon Phi, Maxwell's equations, MPI, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, OpenMP, Performance, Physics, QCD, Tesla K40

November 12, 2014 by hgpu

cuDNN: Efficient Primitives for Deep Learning

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Tesla K40

October 8, 2014 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

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high performance computing on graphics processing units: hgpu.org

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Performance Analysis of Join Algorithms on GPUs

Dynamic Feature-Adaptive Subdivision

Code Optimization on Kepler GPUs and Xeon Phi

cuDNN: Efficient Primitives for Deep Learning

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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