high performance computing on graphics processing units: hgpu.org

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System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

Bo Qiao

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Tags: Code generation, Computer science, CUDA, Image processing, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce RTX 2080, OpenCL, Package, performance portability, Tesla K20, Thesis

January 2, 2022 by hgpu

A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

Shulin Zeng, Guohao Dai, Hanbo Sun, Jun Liu, Shiyao Li, Guangjun Ge, Kai Zhong, Kaiyuan Guo, Yu Wang, Huazhong Yang

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Tags: Cloud, Computer science, CUDA, FPGA, Neural networks, nVidia, Tesla V100, Virtualization

January 2, 2022 by hgpu

A Variant of Concurrent Constraint Programming on GPU

Pierre Talbot, Frederic Pinel, Pascal Bouvry

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Tags: Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Package, Performance

January 2, 2022 by hgpu

GPU-accelerated Faster Mean Shift with euclidean distance metrics

Le You, Han Jiang, Jinyong Hu, Chorng Chang, Lingxi Chen, Xintong Cui, Mengyang Zhao

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Tags: Algorithms, Clustering, CUDA, Image processing, nVidia, nVidia GeForce RTX 3070, nVidia GeForce RTX 3080 Ti, nVidia Quadro P 5000, Package, Pattern recognition, Tesla P100

January 2, 2022 by hgpu

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Michael O'Boyle, Hugh Leather

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Tags: Compilers, Computer science, Flow analysis, Machine learning, OpenCL, Package, Performance

January 2, 2022 by hgpu

COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs

Ruobing Han, Jaewon Lee, Jaewoong Sim, Hyesoon Kim

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia

December 26, 2021 by hgpu

OpenCL-HPX Integration

Michael Schupikov

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 3080, OpenCL, Thesis

December 26, 2021 by hgpu

FSpGEMM: An OpenCL-based HPC Framework for Accelerating General Sparse Matrix-Matrix Multiplication on FPGAs

Erfan Bank Tavakoli, Michael Riera, Masudul Hassan Quraishi, Fengbo Ren

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Tags: Algorithms, Computer science, FPGA, HPC, Linear Algebra, Matrix multiplication, nVidia, nVidia GTX Titan X, OpenCL, Sparse matrix

December 26, 2021 by hgpu

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Seung-Hun Chung

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Tags: Computer science, FPGA, Neural networks, nVidia, nVidia GeForce GTX 1060, OpenCL, Performance, Thesis

December 19, 2021 by hgpu

TCUDB: Accelerating Database with Tensor Processors

Yu-Ching Hu, Yuliang Li, Hung-Wei Tseng

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Tags: Computer science, CUDA, Databases, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 3090

December 19, 2021 by hgpu

Evaluation of Pseudo-Random Number Generation on GPU Cards

Tair Askar, Bekdaulet Shukirgaliyev, Martin Lukac, Ernazar Abdikamalov

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Pseudo-random number generators

December 19, 2021 by hgpu

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Programming

System-Level Optimization and Code Generation for Graphics Processors using a Domain-Specific Language

A Variant of Concurrent Constraint Programming on GPU

GPU-accelerated Faster Mean Shift with euclidean distance metrics

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

COX: CUDA on X86 by Exposing Warp-Level Functions to CPUs

OpenCL-HPX Integration

FSpGEMM: An OpenCL-based HPC Framework for Accelerating General Sparse Matrix-Matrix Multiplication on FPGAs

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

TCUDB: Accelerating Database with Tensor Processors

Evaluation of Pseudo-Random Number Generation on GPU Cards

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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