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A Performance-Portable SYCL Implementation of CRK-HACC for Exascale

Esteban M. Rangel, S. John Pennycook, Adrian Pope, Nicholas Frontiere, Zhiqiang Ma, Varsha Madananth

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Tags: AMD Radeon Instinct MI250X, ATI, Computer science, Cosmology, CUDA, HIP, HPC, nVidia, nVidia A100, Performance, performance portability, Physics, SYCL

October 29, 2023 by hgpu

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu

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Tags: Computer science, CUDA, Deep learning, Evolutionary Computations, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia P100

October 29, 2023 by hgpu

SYCL in the Edge: Performance Evaluation for Heterogeneous Acceleration

Youssef Faqir-Rhazoui, Carlos García

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Jetson Nano, Optical flow, Package, SYCL

October 22, 2023 by hgpu

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

LeXInt: GPU-accelerated Exponential Integrators package

Pranab J. Deka, Alexander Moriggl, Lukas Einkemmer

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Tags: CUDA, Differential equations, Mathematics, nVidia, nVidia A100, Package

October 22, 2023 by hgpu

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Jason Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland

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Tags: AMD Radeon Pro V620, ATI, Chemical Physics, CUDA, HIP, Machine learning, Molecular dynamics, Molecular simulation, nVidia, nVidia A100, nVidia GeForce RTX 4080, OpenCL, Package, Physics

October 15, 2023 by hgpu

Reverse-Mode AD of Reduce-by-Index and Scan in Futhark

Lotte Maria Bruun, Ulrik Stuhr Larsen, Nikolaj Hinnerskov, Cosmin Oancea

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Tags: Computer science, nVidia, nVidia A100, Programming Languages

October 15, 2023 by hgpu

Comparison of different n-body algorithms on various hardware platforms using SYCL

Tim Thüring

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Tags: AMD Radeon Pro VII, Astrophysics, ATI, Computer science, N-body simulation, nVidia, nVidia A100, nVidia GeForce RTX 3090, nVidia Quadro GP100, Package, Physics, SYCL, Thesis

October 15, 2023 by hgpu

Open SYCL on heterogeneous GPU systems: A case of study

Rocío Carratalá-Sáez, Francisco J. andújar, Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, Package, performance portability, SYCL

October 15, 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

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

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

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

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

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

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