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Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

JooYoung Park, DoangJoo Synn, XinYu Piao, Jong-Kook Kim

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Performance, Prefetch

October 16, 2022 by hgpu

OpenMP Offloading in the Jetson Nano Platform

Ilias K. Kasmeridis, Vassilios V. Dimakopoulos

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia Jetson Nano, OpenMP, Package

October 16, 2022 by hgpu

PMT: Power Measurement Toolkit

Stefano Corda, Bram Veenboer, Emma Tolley

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Tags: AMD Radeon Pro W6600, ATI, Computer science, Energy-efficient computing, HPC, nVidia, nVidia Titan RTX, Package, Performance

October 16, 2022 by hgpu

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Hanqiu Chen, Yahya Alhinai, Yihan Jiang, Eunjee Na, Cong Hao

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia RTX A6000, Package

October 16, 2022 by hgpu

Towards Performance Portable Programming for Distributed Heterogeneous Systems

Polykarpos Thomadakis, Nikos Chrisochoides

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance, performance portability, Tesla V100

October 9, 2022 by hgpu

Decompiling x86 Deep Neural Network Executables

Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package, Security

October 9, 2022 by hgpu

Performance portability study of epistasis detection using SYCL on NVIDIA GPU

Zheming Jin, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Genomics, nVidia, performance portability, SYCL, Tesla V100

October 9, 2022 by hgpu

cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs

Tao Lu, Chengkun Wei, Ruijing Yu, Yi Chen, Li Wang, Chaochao Chen, Zeke Wang, and Wenzhi Chen

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Elliptic curves, Machine learning, nVidia, nVidia GeForce GTX 3060, Security, Tesla V100

October 9, 2022 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

An OpenCL-Based FPGA Accelerator for Faster R-CNN

Jianjing An, Dezheng Zhang, Ke Xu, Dong Wang

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Tags: Computational Complexity, Computer science, Deep learning, Design space exploration, FPGA, Neural networks, nVidia, OpenCL, Package, RNN, Tesla K40

October 2, 2022 by hgpu

Efficient Quantized Sparse Matrix Operations on Tensor Cores

Shigang Li, Kazuki Osawa, Torsten Hoefler

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Mixed precision, nVidia, nVidia A100, Package, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Jieyang Chen, Chenhao Xie, Jesun S Firoz, Jiajia Li, Shuaiwen Leon Song, Kevin Barker, Mark Raugas, Ang Li

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, OpenMP, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

OpenMP Offloading in the Jetson Nano Platform

PMT: Power Measurement Toolkit

Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU

Towards Performance Portable Programming for Distributed Heterogeneous Systems

Decompiling x86 Deep Neural Network Executables

Performance portability study of epistasis detection using SYCL on NVIDIA GPU

cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs

Benchmarking optimization algorithms for auto-tuning GPU kernels

An OpenCL-Based FPGA Accelerator for Faster R-CNN

Efficient Quantized Sparse Matrix Operations on Tensor Cores

MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Recent source codes

FP4 FlashAttention forward, backward, and TorchTitan reproduction code

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

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