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Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Anton Rydahl, Joseph Huber, Ethan Luis McDonough, Johannes Doerfert

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Tags: AMD Radeon Instinct MI250X, AMD Radeon Instinct Mi50, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, Numerical Analysis, OpenMP, Performance, Tesla A100, Tesla V100

December 18, 2023 by hgpu

Efficiently Processing Large Relational Joins on GPUs

Bowen Wu, Dimitrios Koutsoukos, Gustavo Alonso

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Tags: Benchmarking, Computer science, CUDA, Databases, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package

December 10, 2023 by hgpu

GenVectorX: A performance-portable SYCL library for Lorentz Vectors operations

Monica Dessole, Jolly Chen, Axel Naumann

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Tags: CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3060, nVidia L4, oneAPI, Package, Performance, Physics, SYCL

December 10, 2023 by hgpu

Understanding the Topics and Challenges of GPU Programming by Classifying and Analyzing Stack Overflow Posts

Wenhua Yang, Chong Zhang, Minxue Pan

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Tags: Computer science, CUDA, nVidia, OpenCL, Programming Languages, survey

December 10, 2023 by hgpu

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, Design space exploration, Hardware Architecture, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia H100, OpenCL, survey

December 3, 2023 by hgpu

Testing and Mutation Testing for GPU Kernels

Yujie Liu

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Tags: Benchmarking, Computer science, CUDA, nVidia, Package, Performance, Thesis

December 3, 2023 by hgpu

A Review of the Parallelization Strategies for Iterative Algorithms

Xingxing Zhou, Ming Ling, Shidi Tang, YanXiang Zhu

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Tags: Algorithms, Computational Complexity, Computer science, CUDA, Data parallelism, OpenCL

December 3, 2023 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

Jun Chen, Xule Zhou, Hyesoon Kim

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Tags: AMD Radeon Instinct MI210, ATI, Code generation, Computer science, CUDA, HIP, nVidia, OpenCL, Package

December 3, 2023 by hgpu

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

Benjamin Brock, Aydın Buluç, Katherine Yelick

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, nVidia DGX-2, Sparse matrix, Tesla V100

December 3, 2023 by hgpu

GT4Py: High Performance Stencils for Weather and Climate Applications using Python

Enrique G. Paredes, Linus Groner, Stefano Ubbiali, Hannes Vogt, Alberto Madonna, Kean Mariotti, Felipe Cruz, Lucas Benedicic, Mauro Bianco, Joost VandeVondele, Thomas C. Schulthess

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Tags: CUDA, Earth and Space Sciences, nVidia, Package, performance portability, Python, Stencil computation, Tesla P100, Weather prediction

November 27, 2023 by hgpu

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

Robin Kobus

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Tags: Bioinformatics, Biology, CUDA, Databases, Hashing, nVidia, nVidia Quadro GV100, Package, Sequence alignment, Tesla P100, Tesla V100, Thesis

November 27, 2023 by hgpu

FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification

Wenqing Wu

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, nVidia GeForce RTX 4080 Ti, nVidia H100, Task scheduling

November 27, 2023 by hgpu

high performance computing on graphics processing units: hgpu.org

Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Efficiently Processing Large Relational Joins on GPUs

GenVectorX: A performance-portable SYCL library for Lorentz Vectors operations

Understanding the Topics and Challenges of GPU Programming by Classifying and Analyzing Stack Overflow Posts

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Testing and Mutation Testing for GPU Kernels

A Review of the Parallelization Strategies for Iterative Algorithms

CuPBoP-AMD: Extending CUDA to AMD Platforms

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs

GT4Py: High Performance Stencils for Weather and Climate Applications using Python

Accelerating bioinformatics applications on CUDA-enabled multi-GPU systems

FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification

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