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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

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

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

Strega: An HTTP Server for FPGAs

Fabio Maschi, Gustavo Alonso

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Tags: Computer science, FPGA, OpenCL, Package

October 15, 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

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

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

Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library

Xinzhe Wu, Edoardo Di Napoli

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Tags: Algorithm optimization, Computer science, CUDA, MPI, nVidia, nVidia A100, OpenMP, OpenMPI, Package

October 8, 2023 by hgpu

Impacts of Parallel Programming on Limited-Resource Hardware

Renato Barreto Hoffmann Filho

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Tags: Computer science, CUDA, nVidia, OpenCL, Programming techniques, Thesis

October 8, 2023 by hgpu

Experience Migrating OpenCL to SYCL: A Case Study on Searches for Potential Off-Target Sites of Cas9 RNA-Guided Endonucleases on AMD GPUs

Zheming Jin, Jeffrey S. Vetter

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI60, AMD Radeon VII, ATI, ATI Radeon HD 7870, Bioinformatics, Biology, OpenCL, performance portability, RNA, SYCL

October 1, 2023 by hgpu

Memory Efficient Mixed-Precision Optimizers

Basile Lewandowski, Atli Kosson

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Tags: Computer science, CUDA, Machine learning, Mixed precision, nVidia, nVidia A100, nVidia V100

October 1, 2023 by hgpu

OpenMP Kernel Language Extensions for Performance Portable GPU Codes

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, nVidia, nVidia A100, OpenMP, performance portability

October 1, 2023 by hgpu

HipKittens: Fast and Furious AMD Kernels

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

mt4g: Memory Topology 4 GPUs

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

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