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A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

DawnCC: a Source-to-Source Automatic Parallelizer of C and C++ Programs

Breno Campos Ferreira Guimaraes, Gleison Souza Diniz Mendonca, Fernando Magno Quintao Pereira

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Tags: Code generation, Compilers, Computer science, OpenACC, OpenMP

August 28, 2016 by hgpu

Automatic Generation of OpenCL Code for ARM Architectures

Sergio Afonso, Alejandro Acosta, Francisco Almeida

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Tags: ARM, Code generation, Compilers, Computer science, OpenCL, RenderScript

August 16, 2016 by hgpu

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Michel Steuwer, Toomas Remmelg, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, BLAS, Code generation, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan Black, OpenCL, performance portability

July 8, 2016 by hgpu

Automatic Test Case Reduction for OpenCL

Moritz Pflanzer, Alastair F. Donaldson, Andrei Lascu

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Tags: Code generation, Compilers, Computer science, nVidia, nVidia GeForce GTX Titan, OpenCL

May 3, 2016 by hgpu

Polly-ACC: Transparent compilation to heterogeneous hardware

Tobias Grosser, Torsten Hoefler

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, LLVM, nVidia, nVidia GeForce GT 730 M, nVidia GeForce GTX Titan Black

May 3, 2016 by hgpu

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Andreas Klockner, Lucas C. Wilcox, T. Warburton

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Code generation, Computer science, Fortran, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, Prefetch, Programming Languages, Python, Tesla K40

April 29, 2016 by hgpu

High-level GPU programming in Julia

Tim Besard, Pieter Verstraete, Bjorn De Sutter

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Tags: Code generation, Compilers, Computer science, CUDA, LLVM, nVidia, nVidia GeForce GTX Titan, Programming Languages

April 14, 2016 by hgpu

gpucc: an open-source GPGPU compiler

Jingyue Wu, Artem Belevich, Eli Bendersky, Mark Heffernan, Chris Leary, Jacques Pienaar, Bjarke Roune, Rob Springer, Xuetian Weng, Robert Hundt

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Tags: Code generation, Compilers, Computer science, CUDA, LLVM, nVidia, Presentation, Tesla K40

April 6, 2016 by hgpu

Automatic test case reduction of randomly generated OpenCL kernels

Moritz Pflanzer

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Tags: Code generation, Computer science, LLVM, nVidia, nVidia GeForce GT 630, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, nVidia Quadro K5200, OpenCL, Thesis

April 6, 2016 by hgpu

Implementation of the SYCL Heterogeneous Computing Library

Peter Zuzek

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Tags: Code generation, Computer science, Heterogeneous systems, OpenCL, Package, SYCL, Thesis

April 3, 2016 by hgpu

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Li-Wen Chang, Hee-Seok Kim, Wen-mei Hwu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Performance, Tesla K20

March 15, 2016 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

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

DawnCC: a Source-to-Source Automatic Parallelizer of C and C++ Programs

Automatic Generation of OpenCL Code for ARM Architectures

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Automatic Test Case Reduction for OpenCL

Polly-ACC: Transparent compilation to heterogeneous hardware

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

High-level GPU programming in Julia

gpucc: an open-source GPGPU compiler

Automatic test case reduction of randomly generated OpenCL kernels

Implementation of the SYCL Heterogeneous Computing Library

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

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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