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hgpu.org » Programming » CUDA

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

stdgpu: Efficient STL-like Data Structures on the GPU

Patrick Stotko

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, Package

August 25, 2019 by hgpu

Memory-Efficient Object-Oriented Programming on GPUs

Matthias Springer

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 940 M, nVidia GeForce GTX 980, nVidia GeForce GTX Titan XP, Package, Programming Languages, Thesis

August 25, 2019 by hgpu

Efficient Simulation of Fluid Flow and Transport in Heterogeneous Media Using Graphics Processing Units (GPUs)

Hassan Dashtian, Muhammad Sahimi

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Tags: Computational Physics, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 880, Physics

August 18, 2019 by hgpu

Visual Analysis Algorithms for Embedded Systems

Syed Tahir Hussain Rizvi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson K1, nVidia Jetson TX1, Thesis

August 18, 2019 by hgpu

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

Performance Comparison for Neuroscience Application Benchmarks

Andreas Herten, Thorsten Hater, Wouter Klijn, Dirk Pleiter

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Tags: Benchmarking, Computer science, CUDA, Neuroscience, nVidia, Performance, Tesla P100, Tesla V100

August 11, 2019 by hgpu

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Tesla K40

August 11, 2019 by hgpu

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

Luiz H. Sena, Iury V. Bessa, Mikhail R. Gadelha, Lucas C. Cordeiro, Edjard Mota

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Tags: Computer science, CUDA, Neural networks, nVidia, Package

August 5, 2019 by hgpu

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

Shaoyi Peng, Sheldon X.-D. Tan

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Sparse matrix

August 5, 2019 by hgpu

NNS: The Case For Neural Network-based Sorting

Xiao-ke Zhu, Tai-ning Chen, Jing He, Wei Zhou

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Sorting, TensorFlow

July 28, 2019 by hgpu

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Demystifying the MLPerf Benchmark Suite

Survey and Benchmarking of Machine Learning Accelerators

stdgpu: Efficient STL-like Data Structures on the GPU

Memory-Efficient Object-Oriented Programming on GPUs

Efficient Simulation of Fluid Flow and Transport in Heterogeneous Media Using Graphics Processing Units (GPUs)

Visual Analysis Algorithms for Embedded Systems

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Performance Comparison for Neuroscience Application Benchmarks

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

NNS: The Case For Neural Network-based Sorting

Recent source codes

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

FP4 FlashAttention forward, backward, and TorchTitan reproduction code

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

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