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Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2070, Performance, Python, Tesla P100, Tesla V100

February 7, 2021 by hgpu

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Guei-Yuan Lueh, Kaiyu Chen, Gang Chen, Joel Fuentes, Wei-Yu Chen, Fangwen Fu, Hong Jiang, Hongzheng Li, Daniel Rhee

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

January 31, 2021 by hgpu

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Mike Turner, Jamil Appa, Neil Ashton

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Tags: cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, SYCL, Tesla A100, Tesla T4, Tesla V100

January 31, 2021 by hgpu

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

Aamir Shafi, Jahanzeb Maqbool Hashmi, Hari Subramoni, Dhabaleswar K. (DK) Panda

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Tags: Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Performance, Python, Tesla V100

January 31, 2021 by hgpu

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Weicheng Xue

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Tags: cfd, CUDA, Finite difference, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenACC, Tesla C2075, Tesla P100, Tesla V100, Thesis

January 31, 2021 by hgpu

Easy and Efficient Agent-based Simulations with the OpenABL Language and Compiler

Biagio Cosenza, Nikita Popov, Ben Juurlink, Paul Richmond, Mozhgan Kabiri Chimeh, Carmine Spagnuolo, Gennaro Cordasco, Vittorio Scarano

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Tags: Compilers, Computer science, CUDA, DSL, nVidia, nVidia GeForce GTX Titan XP, Package

January 24, 2021 by hgpu

Performance Analysis and Improvement of Parallel Differential Evolution

Zibin Pan

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Tags: Artificial intelligence, Computer science, CUDA, Differential evolution, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 760

January 24, 2021 by hgpu

Non-Parametric Adaptive Network Pruning

Lin Mingbao, Ji Rongrong, Li Shaojie, Wang Yan, Wu Yongjian, Huang Feiyue, Ye Qixiang

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Tags: Computer science, CUDA, Deep learning, nVidia, Package, PyTorch, Tesla V100

January 24, 2021 by hgpu

Learning Massive Graph Embeddings on a Single Machine

Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

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Tags: Computer science, CUDA, Databases, Graph theory, Machine learning, nVidia, Tesla V100

January 24, 2021 by hgpu

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

Johannes de Fine Licht, Andreas Kuster, Tiziano De Matteis, Tal Ben-Nun, Dominic Hofer, Torsten Hoefler

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Tags: Code generation, Computer science, CUDA, Distributed computing, FPGA, Heterogeneous systems, nVidia, OpenCL, Package, Tesla P100, Tesla V100

January 24, 2021 by hgpu

Efficient Nearest-Neighbor Data Sharing in GPUs

Negin Nematollahi, Mohammad Sadrosadati, Hajar Falahati, Marzieh Barkhordar, Mario Paulo Drumond, Hamid Sarbazi-Azad, Babak Falsafi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Stencil computation

January 10, 2021 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

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high performance computing on graphics processing units: hgpu.org

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach

C-for-Metal: High Performance SIMD Programming on Intel GPUs

Performance of CPU and GPU HPC Architectures for off-design aircraft simulation

Efficient MPI-based Communication for GPU-Accelerated Dask Applications

CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications

Easy and Efficient Agent-based Simulations with the OpenABL Language and Compiler

Performance Analysis and Improvement of Parallel Differential Evolution

Non-Parametric Adaptive Network Pruning

Learning Massive Graph Embeddings on a Single Machine

StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

Efficient Nearest-Neighbor Data Sharing in GPUs

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CLUEstering: a high-performance density-based clustering library for scientific computing

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