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Persistent Kernels for Iterative Memory-bound GPU Applications

Lingqi Zhang, Mohamed Wahib, Peng Chen, Jintao Meng, Xiao Wang, Satoshi Matsuoka

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Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla A100, Tesla V100

April 10, 2022 by hgpu

ALPINIST: An Annotation-Aware GPU Program Optimizer

Ömer Sakar, Mohsen Safari, Marieke Huisman, Anton Wijs

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Tags: Computer science, CUDA, nVidia, OpenCL, Optimization, Package

April 10, 2022 by hgpu

One-shot tuner for deep learning compilers

Jaehun Ryu, Eunhyeok Park, Hyojin Sung

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Tags: Compilers, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 2080 Ti, Package

March 27, 2022 by hgpu

Advanced Joins on GPUs

Christos Bellas

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX Titan XP, Package, Thesis

March 27, 2022 by hgpu

Simulation Methodologies for Mobile GPUs

Kuba Kaszyk

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Tags: Computer science, Design space exploration, OpenCL, simulation, Thesis

March 27, 2022 by hgpu

Data transfer optimizations for heterogeneous managed runtime systems

Florin-Gabriel Blanaru

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Tags: Computer science, CUDA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1650, nVidia Quadro GP100, OpenCL

March 27, 2022 by hgpu

Machine Learning for CUDA+MPI Design Rules

Carl Pearson, Aurya Javeed, Karen Devine

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Tags: Computer science, CUDA, Machine learning, Monte Carlo simulation, MPI, nVidia, nVidia A100

March 20, 2022 by hgpu

Managing Extreme Heterogeneity in Next Generation HPC Systems

Harsh Khetawat

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Tags: Computer science, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 2060, OpenCL, Thesis

March 20, 2022 by hgpu

Concurrent CPU-GPU Task Programming using Modern C++

Tsung-Wei Huang, Yibo Lin

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 2080, Timing analysis

March 20, 2022 by hgpu

DISTAL: The Distributed Tensor Algebra Compiler

Rohan Yadav, Alex Aiken, Fredrik Kjolstad

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla V100

March 20, 2022 by hgpu

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

Vincent R. Pascuzzi, Mehdi Goli

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, FFT, Heterogeneous systems, HIP, HPC, nVidia, nVidia A100, OpenCL, performance portability, SYCL

March 20, 2022 by hgpu

HipBone: A performance-portable GPU-accelerated C++ version of the NekBone benchmark

Noel Chalmers, Abhishek Mishra, Damon McDougall, Tim Warburton

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Benchmarking, Computer science, CUDA, HIP, HPC, MPI, nVidia, Package, Performance, Tesla V100

March 11, 2022 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

Persistent Kernels for Iterative Memory-bound GPU Applications

ALPINIST: An Annotation-Aware GPU Program Optimizer

One-shot tuner for deep learning compilers

Advanced Joins on GPUs

Simulation Methodologies for Mobile GPUs

Data transfer optimizations for heterogeneous managed runtime systems

Machine Learning for CUDA+MPI Design Rules

Managing Extreme Heterogeneity in Next Generation HPC Systems

Concurrent CPU-GPU Task Programming using Modern C++

DISTAL: The Distributed Tensor Algebra Compiler

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

HipBone: A performance-portable GPU-accelerated C++ version of the NekBone benchmark

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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