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Characterizing CUDA and OpenMP Synchronization Primitives

Brandon Alexander Burtchell, Martin Burtscher

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 2070, nVidia GeForce RTX 4090, OpenMP, Package, Performance

August 25, 2024 by hgpu

Memory Interference and Performance Prediction in GPU-Accelerated Heterogeneous Systems

Alessio Masola

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce RTX 2070, nVidia GeForce RTX 2080, nVidia Jetson AGX Xavier, Rendering, Thesis, Visualization

June 9, 2024 by hgpu

Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

Manuel Costanzo, Enzo Rucci, Carlos García-Sanchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computational biology, CUDA, Heterogeneous systems, Intel Arc A770, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, nVidia V100, oneAPI, Package, Sequence alignment, SYCL

February 25, 2024 by hgpu

Minuet: Accelerating 3D Sparse Convolutions on GPUs

Jiacheng Yang, Christina Giannoula, Jun Wu, Mostafa Elhoushi, James Gleeson, Gennady Pekhimenko

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Tags: Cloud, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 2070, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, Package, Performance, PyTorch, Tesla A100

January 21, 2024 by hgpu

HiRace: Accurate and Fast Source-Level Race Checking of GPU Programs

John Jacobson, Martin Burtscher, Ganesh Gopalakrishnan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce RTX 2070

January 14, 2024 by hgpu

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: AMD Radeon RX 6700 XT, ATI, Bioinformatics, Biology, Computer science, CUDA, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3070, nVidia GeForce RTX 3090, oneAPI, Programming Languages, Smith-Waterman algorithm, SYCL, Tesla V100

September 24, 2023 by hgpu

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs

Taabish Jeshani

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce RTX 2070, Package, Performance, Thesis

May 7, 2023 by hgpu

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, Heterogeneous systems, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, OpenCL, Sequence alignment, SYCL, Tesla V100

November 27, 2022 by hgpu

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Mohammad K. Dashti

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Memory model, nVidia, nVidia GeForce RTX 2070, nVidia Jetson TK1, OpenCL, Package, Thesis

August 28, 2022 by hgpu

The Celerity High-level API: C++20 for Accelerator Clusters

Peter Thoman, Florian Tischler, Philip Salzmann, Thomas Fahringer

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Tags: Code generation, Computer science, Data parallelism, MPI, nVidia, nVidia GeForce RTX 2070, Package, Programming techniques, SYCL

May 1, 2022 by hgpu

Migrating CUDA to oneAPI: A Smith-Waterman Case Study

Manuel Costanzo, Enzo Rucci, Carlos Garcia Sanchez, Marcelo Naiouf, Manuel Prieto-Matias

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Tags: Bioinformatics, Biology, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, Sequence alignment, Smith-Waterman algorithm, SYCL

March 27, 2022 by hgpu

High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

Cyrille Rossant, Nicolas Rougier

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Tags: Computer science, nVidia, nVidia GeForce RTX 2070, Package, Python, Rendering, Visualization, Vulkan

December 26, 2021 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Characterizing CUDA and OpenMP Synchronization Primitives

Memory Interference and Performance Prediction in GPU-Accelerated Heterogeneous Systems

Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

HiRace: Accurate and Fast Source-Level Race Checking of GPU Programs

Comparing Performance and Portability between CUDA and SYCL for Protein Database Search on NVIDIA, AMD, and Intel GPUs

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

The Celerity High-level API: C++20 for Accelerator Clusters

Migrating CUDA to oneAPI: A Smith-Waterman Case Study

High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

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Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

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