high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Glow: Graph Lowering Compiler Techniques for Neural Networks

Nadav Rotem, Jordan Fix, Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, James Hegeman, Roman Levenstein, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, Misha Smelyanskiy

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Tags: Code generation, Compilers, Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Machine learning, Neural networks, nVidia, Package

May 20, 2018 by hgpu

OpenCL Vector Swizzling Optimization under Global Value Numbering

Li-An Her, Jenq-Kuen Lee

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Tags: Computer science, Heterogeneous systems, HSA, LLVM, OpenCL

May 20, 2018 by hgpu

Neural Multi-scale Image Compression

Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara

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Tags: Compression, Computer vision, CUDA, Deep learning, Image processing, Machine learning, nVidia

May 20, 2018 by hgpu

Deep Neural Machine Translation with Weakly-Recurrent Units

Mattia Antonino Di Gangi, Marcello Federico

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Tags: Computer science, CUDA, NLP, nVidia, nVidia GeForce GTX 1080, Package, RNN, Tesla K80, Torch

May 20, 2018 by hgpu

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

Beau Johnston, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon HD 7970, AMD Radeon R9 290X, AMD Radeon R9 Fury X, AMD Radeon RX 480, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Tesla K20, Tesla K40

May 12, 2018 by hgpu

EngineCL: Usability and Performance in Heterogeneous Computing

Raul Nozal, Jose Luis Bosque, Ramon Beivide

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Tags: AMD Radeon R7, ATI, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, performance portability, Tesla K20

May 12, 2018 by hgpu

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Susmitha Gogineni

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, OpenCL, Thesis

May 12, 2018 by hgpu

Simulating Quantum Computers Using OpenCL

Adam Kelly

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Tags: Computer science, nVidia, OpenCL, Package, Quantum computing, Quantum Physics, Tesla V100

May 5, 2018 by hgpu

Tiramisu: A Code Optimization Framework for High Performance Systems

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

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Tags: Algorithms, Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, Linear Algebra, LLVM, MPI, nVidia, OpenMPI, Tesla K40

May 5, 2018 by hgpu

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dan Lin

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Tags: Algorithms, Computer science, Data parallelism, LLVM, nVidia, nVidia GeForce GTX 950, Package, PTX, Thesis

April 28, 2018 by hgpu

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman

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Tags: AMD Radeon R9 390, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation

April 28, 2018 by hgpu

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

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

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

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high performance computing on graphics processing units: hgpu.org

Programming

Glow: Graph Lowering Compiler Techniques for Neural Networks

OpenCL Vector Swizzling Optimization under Global Value Numbering

Neural Multi-scale Image Compression

Deep Neural Machine Translation with Weakly-Recurrent Units

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

EngineCL: Usability and Performance in Heterogeneous Computing

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Efficient Hardware Acceleration on SoC-FPGA with OpenCL

Simulating Quantum Computers Using OpenCL

Tiramisu: A Code Optimization Framework for High Performance Systems

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Recent source codes

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

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

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

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

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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