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Performance/power assessment of CNN packages on embedded automotive platforms

Paolo Burgio, Gianluca Brilli

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Tags: CNN, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson Nano, nVidia Jetson TX2, Performance

October 22, 2023 by hgpu

Performance portability analysis of SYCL with a classical CG on CPU, GPU, and FPGA

Julian Franquinet

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Tags: Benchmarking, Computer science, CUDA, FPGA, nVidia, nVidia Quadro GP100, Optimization, Performance, performance portability, SYCL

October 22, 2023 by hgpu

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

Automated Buffer Sizing of Dataflow Applications in a High-Level Synthesis Workflow

Alexandre Honorat, Mickaël Dardaillon, Hugo Miomandre, Jean-François Nezan

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Tags: Code generation, Computer science, FPGA, HLS, OpenCL, Package, Performance

October 8, 2023 by hgpu

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

Nick Brown, Maurice Jamieson, Anton Lydike, Emilien Bauer, Tobias Grosser

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Tags: Computer science, Fortran, HPC, LLVM, MPI, nVidia, Package, Performance, Tesla V100

October 8, 2023 by hgpu

Improving the Efficiency of OpenCL Kernels through Pipes

Mostafa Eghbali Zarch, Michela Becchi

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Tags: Computer science, FPGA, OpenCL, Performance, performance portability

September 17, 2023 by hgpu

Fortran High-Level Synthesis: Reducing the barriers to accelerating HPC codes on FPGAs

Gabriel Rodriguez-Canal, Nick Brown, Tim Dykes, Jessica R. Jones, Utz-Uwe Haus

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Tags: Computer science, Fortran, FPGA, HLS, HPC, OpenCL, Performance

September 6, 2023 by hgpu

Sieve: Stratified GPU-Compute Workload Sampling

Mahmood Naderan-Tahan, Hossein SeyyedAghaei, Lieven Eeckhout

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3080, Package, Performance

August 28, 2023 by hgpu

Static and Dynamic Analyses for Efficient GPU Execution

Philip Munksgaard

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Performance, Thesis

August 13, 2023 by hgpu

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Pietro Incardona, Aryaman Gupta, Serhii Yaskovets, Ivo F. Sbalzarini

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenACC, OpenCL, OpenMP, Package, Performance, performance portability, SYCL

July 30, 2023 by hgpu

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Shilei Tian, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Computer science, CUDA, LLVM, nVidia, nVidia A100, OpenMP, Performance

July 24, 2023 by hgpu

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Mingyu Liang, Wenyin Fu, Louis Feng, Zhongyi Lin, Pavani Panakanti, Shengbao Zheng, Srinivas Sridharan, Christina Delimitrou

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Tags: AI, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Performance, PyTorch, Tesla A100, Tesla V100

July 16, 2023 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

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high performance computing on graphics processing units: hgpu.org

Performance/power assessment of CNN packages on embedded automotive platforms

Performance portability analysis of SYCL with a classical CG on CPU, GPU, and FPGA

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Automated Buffer Sizing of Dataflow Applications in a High-Level Synthesis Workflow

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

Improving the Efficiency of OpenCL Kernels through Pipes

Fortran High-Level Synthesis: Reducing the barriers to accelerating HPC codes on FPGAs

Sieve: Stratified GPU-Compute Workload Sampling

Static and Dynamic Analyses for Efficient GPU Execution

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Maximizing Parallelism and GPU Utilization For Direct GPU Compilation Through Ensemble Execution

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks

Recent source codes

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

TRUST: a thermalhydraulic software package for CFD simulations

Modular: The Modular Platform (includes MAX & Mojo)

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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