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Fancier: A Unified Framework for Java, C, and OpenCL Integration

Sergio Afonso, Francisco Almeida

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Tags: Computer science, Image processing, Java, OpenACC, OpenCL, Package, Performance

January 16, 2022 by hgpu

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Dengpan Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Operating systems, PTX, SYCL, Thesis

January 16, 2022 by hgpu

Studying the Potential of Automatic Optimizations in the Intel FPGA SDK for OpenCL

Adel Ejjeh, Vikram Adve, Rob Rutenbar

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Tags: Computer science, Design space exploration, FPGA, HLS, OpenCL

January 16, 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

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

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

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

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Studying the Potential of Automatic Optimizations in the Intel FPGA SDK for OpenCL

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

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

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

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

High performance computing on Android devices – a case study

GPU backed Data Mining on Android Devices

Recent source codes

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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