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Compyle: a Python package for parallel computing

Aditya Bhosale, Prabhu Ramachandran

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Tags: Code generation, Computer science, CUDA, HPC, nVidia, OpenCL, Package, Python, Tesla P100

July 19, 2020 by hgpu

Investigating Input Representations and Representation Models of Source Code for Machine Learning

Alexander Brauckmann

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Tags: AMD Radeon HD 7970, ATI, Code generation, Computer science, LLVM, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 970, OpenCL, Thesis

July 12, 2020 by hgpu

Automated Partitioning of Data-Parallel Kernels using Polyhedral Compilation

Alexander Matz, Johannes Doerfert, Holger Fröning

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Tags: Code generation, Computer science, CUDA, LLVM, nVidia, Package, Tesla K80

July 12, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 by hgpu

From Constraint Programming to Heterogeneous Parallelism

Philip Ginsbach

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Tags: AMD Radeon R7, ATI, Code generation, Computer science, DSL, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenMP, Thesis

May 10, 2020 by hgpu

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

Gleison Souza Diniz Mendonça, Chunhua Liao, Fernando Magno Quintão Pereira

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Tags: Benchmarking, Code generation, Computer science, OpenMP, Package

May 3, 2020 by hgpu

Accelerating Winograd Convolutions using Symbolic Computation and Meta-programming

Arya Mazaheri, Tim Beringer, Matthew Moskewicz, Felix Wolf, Ali Jannesari

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Tags: AMD Radeon RX 580, ATI, Code generation, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance, performance portability, Symbolic Computation, Vulkan

April 26, 2020 by hgpu

AvA: Accelerated Virtualization of Accelerators

Hangchen Yu, Arthur Michener Peters, Amogh Akshintala, Christopher J. Rossbach

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Tags: AMD Radeon RX 580, AMD RX Vega 64, ATI, Cloud, Code generation, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Virtualization

March 1, 2020 by hgpu

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

Ari Rasch, Richard Schulze, Sergei Gorlatch

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Tags: Code generation, Compilers, Computer science, nVidia, OpenCL, Package, performance portability, Tesla V100

January 19, 2020 by hgpu

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Ryan Senanayake, Fredrik Kjolstad, Changwan Hong, Shoaib Kamil, Saman Amarasinghe

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Tags: Code generation, Compilers, Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, Package, Sparse matrix, Tesla V100

January 5, 2020 by hgpu

Deep Learning Based FPGA-CPU Acceleration

R. Luis Jalabert

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Tags: Code generation, Computer science, Deep learning, FPGA, Machine learning, Neural networks, Package, Thesis

November 17, 2019 by hgpu

Code Optimization on GPUs

Changwan Hong

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Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

Compyle: a Python package for parallel computing

Investigating Input Representations and Representation Models of Source Code for Machine Learning

Automated Partitioning of Data-Parallel Kernels using Polyhedral Compilation

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

From Constraint Programming to Heterogeneous Parallelism

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

Accelerating Winograd Convolutions using Symbolic Computation and Meta-programming

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Deep Learning Based FPGA-CPU Acceleration

Code Optimization on GPUs

Recent source codes

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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