high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

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

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

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

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

CitiusSynapse: A Deep Learning Framework for Embedded Systems

Seungtae Hong, Hyunwoo Cho, Jeong-Si Kim

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Tags: Computer science, Deep learning, Embedded high-performance computing, OpenCL

December 12, 2021 by hgpu

Fast Neural Representations for Direct Volume Rendering

Sebastian Weiss, Philipp Hermüller, Rüdiger Westermann

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Tags: 3D Graphics and Realism, CUDA, GLSL, nVidia, nVidia GeForce RTX 2070, Package, Raytracing, Rendering, Visualization

December 12, 2021 by hgpu

High performance computing on Android devices – a case study

Robert Fritze, Claudia Plant

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Tags: AMD Radeon VII, Android, ATI, Benchmarking, Computer science, HPC, nVidia GeForce Titan V, OpenCL

December 12, 2021 by hgpu

GPU backed Data Mining on Android Devices

Robert Fritze, Claudia Plant

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Tags: Android, Computer science, Data mining, HPC, Machine learning, OpenCL, Package

December 12, 2021 by hgpu

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Computer science, CUDA, Machine learning, NLP, nVidia

December 5, 2021 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Programming

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

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

OpenCL-HPX Integration

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

CitiusSynapse: A Deep Learning Framework for Embedded Systems

Fast Neural Representations for Direct Volume Rendering

High performance computing on Android devices – a case study

GPU backed Data Mining on Android Devices

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Recent source codes

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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