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Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Akihiro Hayashi, Kazuaki Ishizaki, Vivek Sarkar, Gita Koblents

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Tags: Code generation, Compilers, Computer science, CUDA, Java, Machine learning, nVidia, OpenCL, Tesla K40

October 27, 2015 by ahayashi

A Co-Design Framework with OpenCL Support for Low-Energy Wide SIMD Processor

Dongrui She, Yifan He, Luc Waeijen, Henk Corporaal

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Tags: Code generation, Compilers, Computer science, Embedded high-performance computing, OpenCL

September 24, 2015 by hgpu

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Kazuhiko Ohno, Tomoharu Kamiya, Takanori Maruyama, Masaki Matsumoto

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Tags: Benchmarking, Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 980

July 15, 2015 by hgpu

Many-Core Compiler Fuzzing

Christopher Lidbury, Andrei Lascu, Nathan Chong, Alastair F. Donaldson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Compilers, Computer science, FPGA, Intel HD 4600, Intel Xeon Phi, nVidia, nVidia GeForce GTX 770, nVidia GeForce GTX Titan, OpenCL, Tesla K40, Tesla M2050

July 10, 2015 by hgpu

Falcon: A Graph Manipulation Language for Heterogeneous Systems

Unnikrishnan C, Y N Srikant, Rupesh Nasre

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Tags: Compilers, Computer science, CUDA, Graph theory, Heterogeneous systems, nVidia, OpenMP, Tesla C2050, Tesla C2075, Tesla K20

June 16, 2015 by hgpu

OMP2HMPP: Compiler Framework for Energy-Performance Trade-off Analysis of Automatically Generated Codes

Albert Saa-Garriga, David Castells-Rufas, Jordi Carrabina

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Tesla M2050

May 5, 2015 by hgpu

Generating Performance Portable Code using Rewrite Rules: From High-level Functional Expressions to High-Performance OpenCL Code

Michel Steuwer, Christian Fensch, Sam Lindley, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL

March 8, 2015 by hgpu

Compiler-Level Explicit Cache for a GPGPU Programming Framework

Tomoharu Kamiya, Takanori Maruyama, Kazuhiko Ohno, Masaki Matsumoto

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Tesla C2050, Tesla K20

December 12, 2014 by hgpu

A Validation Testsuite for OpenACC 1.0

Cheng Wang, Rengan Xu, Sunita Chandrasekaran, Barbara Chapman, Oscar Hernandez

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Tags: Compilers, Computer science, Fortran, nVidia, OpenACC, Tesla K20

September 9, 2014 by hgpu

A CUDA Back-End for the Equelle Compiler

Havard Heitlo Holm

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Tags: Compilers, Computer science, CUDA, Differential equations, Finite volume method, Heterogeneous systems, nVidia, nVidia Quadro NVS 5200 M, Package, Partial differential equations, PDEs, Tesla K40, Thesis

September 8, 2014 by hgpu

Directive-Based Compilers for GPUs

Swapnil Ghike, Ruben Gran, Maria J. Garzaran, David Padua

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenACC, Tesla X2090

September 2, 2014 by hgpu

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Moritz Schmid, Oliver Reiche, Christian Schmitt, Frank Hannig, Jurgen Teich

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Tags: ARM, Code generation, Compilers, Computational Complexity, Computer science, Differential equations, FPGA, Image processing, Partial differential equations, PDEs

August 23, 2014 by hgpu

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

PhysProver: Advancing Automatic Theorem Proving for Physics

See all packages

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high performance computing on graphics processing units: hgpu.org

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

A Co-Design Framework with OpenCL Support for Low-Energy Wide SIMD Processor

Automatic Optimization of Thread Mapping for a GPGPU Programming Framework

Many-Core Compiler Fuzzing

Falcon: A Graph Manipulation Language for Heterogeneous Systems

OMP2HMPP: Compiler Framework for Energy-Performance Trade-off Analysis of Automatically Generated Codes

Generating Performance Portable Code using Rewrite Rules: From High-level Functional Expressions to High-Performance OpenCL Code

Compiler-Level Explicit Cache for a GPGPU Programming Framework

A Validation Testsuite for OpenACC 1.0

A CUDA Back-End for the Equelle Compiler

Directive-Based Compilers for GPUs

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Recent source codes

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

PhysProver: Advancing Automatic Theorem Proving for Physics

Most viewed papers (last 30 days)