high performance computing on graphics processing units: hgpu.org

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Securing GPU via Region-based Bounds Checking

Jaewon Lee, Yonghae Kim, Jiashen Cao, Euna Kim, Jaekyu Lee, Hyesoon Kim

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080, OpenCL, Security

June 19, 2022 by hgpu

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Wei Sun, Ang Li, Tong Geng, Sander Stuijk, Henk Corporaal

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Tags: Benchmarking, Computer science, Matrix multiplication, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, PTX, Sparse matrix

June 12, 2022 by hgpu

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

Nicolas Weber

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Tags: AI, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080, Performance

May 29, 2022 by hgpu

PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

Alessandro Berti, Minh Phan Nghia, Wil M.P. van der Aalst

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Tags: Computer science, Databases, nVidia, nVidia GeForce RTX 2080, Package, Python

April 17, 2022 by hgpu

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

Guohao Dai, Guyue Huang, Shang Yang, Zhongming Yu, Hengrui Zhang, Yufei Ding, Yuan Xie, Huazhong Yang, Yu Wang

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Tags: Algorithms, CUDA, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

February 20, 2022 by hgpu

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

An Auto-Programming Approach to Vulkan

Vladimir Frolov, Vadim Sanzharov, Vladimir Galaktionov, Alexandr Scherbakov

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Tags: 3D Graphics and Realism, Code generation, nVidia, nVidia GeForce RTX 2080, Raytracing, Vulkan

December 5, 2021 by hgpu

Block Conjugate Gradient Solver in OpenCL

Kert Tali, Eero Vainikko

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Tags: Algorithms, Computer science, Conjugate gradient solver, Linear Algebra, nVidia, nVidia GeForce RTX 2080, OpenCL

July 11, 2021 by hgpu

Using hardware performance counters to speed up autotuning convergence on GPUs

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, OpenCL, Package, Performance, Vulkan

February 23, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

AZP: Automatic Specialization for Zero Values in Gaming Applications

Mark W. Stephenson, Ram Rangan

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Tags: Computer science, Games, HLSL, nVidia, nVidia GeForce RTX 2080, OpenGL, Performance, Vulkan

November 29, 2020 by hgpu

Cross-platform programming model for many-core lattice Boltzmann simulations

Jonas Latt, Christophe Coreixas, Joël Beny

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Tags: Algorithms, cfd, Computational Physics, CUDA, Fluid dynamics, Heterogeneous systems, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 1080, nVidia GeForce RTX 2080, Physics

October 25, 2020 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

Securing GPU via Region-based Bounds Checking

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks

PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

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

An Auto-Programming Approach to Vulkan

Block Conjugate Gradient Solver in OpenCL

Using hardware performance counters to speed up autotuning convergence on GPUs

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

AZP: Automatic Specialization for Zero Values in Gaming Applications

Cross-platform programming model for many-core lattice Boltzmann simulations

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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