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Parallel Arbitrary-precision Integer Arithmetic

Davood Mohajerani

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenCL, Package, Thesis

April 5, 2021 by hgpu

Daisen: A Framework for Visualizing Detailed GPU Execution

Yifan Sun, Yixuan Zhang, Ali Mosallaei, Michael D. Shah, Cody Dunne, David Kaeli

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Tags: AMD R9 Nano, Artificial intelligence, ATI, Computer science, CUDA, Hardware Architecture, nVidia, OpenCL, Visualization

April 5, 2021 by hgpu

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Benchmarking, Computer science, CUDA, Databases, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 980, Tesla T4

April 5, 2021 by hgpu

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters

Xinxin Mei, Qiang Wang, Xiaowen Chu, Hai Liu, Yiu-Wing Leung, Zongpeng Li

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Tags: Computer science, Deep learning, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, Package, Task scheduling

April 5, 2021 by hgpu

Enabling OpenMP Task Parallelism on Multi-FPGAs

R. Nepomuceno, R. Sterle, G. Valarini, M. Pereira, H. Yviquel, G. Araujo

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Tags: Computer science, FPGA, OpenMP

March 28, 2021 by hgpu

Accelerating Deep Neural Networks implementation: A survey

Meriam Dhouibi, Ahmed Karim Ben Salem, Afef Saidi, Slim Ben Saoud

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Tags: ASIC, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL, Optimization, Performance, survey

March 28, 2021 by hgpu

Accelerating Sparse Approximate Matrix Multiplication on GPUs

Xiaoyan Liu, Yi Liu, Ming Dun, Bohong Yin, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla V100

March 28, 2021 by hgpu

De-specializing an HLS library for Deep Neural Networks: improvements upon hls4ml

Serena Curzel, Nicolò Ghielmetti, Michele Fiorito, Fabrizio Ferrandi

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Tags: Computer science, FPGA, Hardware Architecture, Image recognition, Machine learning, Neural networks, Physics

March 28, 2021 by hgpu

CUDA Tutorial – Cryptanalysis of Classical Ciphers Using Modern GPUs and CUDA

Miroslav Dimitrov, Bernhard Esslinger

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Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 1060, Package, Security, Tutorial

March 28, 2021 by hgpu

Porting a sparse linear algebra math library to Intel GPUs

Yuhsiang M. Tsai, Terry Cojean, Hartwig Anzt

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Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, HIP, Linear Algebra, nVidia, Package, Performance, Sparse, Sparse matrix, Tesla V100

March 21, 2021 by hgpu

GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments

Jihun Kang, Heonchang Yu

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Tags: AMD Radeon RX 570, ATI, Cloud, Computer science, HPC, OpenCL, Task scheduling, Virtualization

March 21, 2021 by hgpu

AVEC: Accelerator Virtualization in Cloud-Edge Computing for Deep Learning Libraries

Jason Kennedy, Blesson Varghese, Carlos Reaño

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Tags: Cloud, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TX2, Virtualization

March 21, 2021 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Parallel Arbitrary-precision Integer Arithmetic

Daisen: A Framework for Visualizing Detailed GPU Execution

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters

Enabling OpenMP Task Parallelism on Multi-FPGAs

Accelerating Deep Neural Networks implementation: A survey

Accelerating Sparse Approximate Matrix Multiplication on GPUs

De-specializing an HLS library for Deep Neural Networks: improvements upon hls4ml

CUDA Tutorial – Cryptanalysis of Classical Ciphers Using Modern GPUs and CUDA

Porting a sparse linear algebra math library to Intel GPUs

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

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