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Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Martin Uray, Eduard Hirsch, Gerold Katzinger, Michael Gadermayr

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A6000

October 17, 2021 by hgpu

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Yue Niu, Ramy E. Ali, Salman Avestimehr

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Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 5000, Security

October 10, 2021 by hgpu

Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations

Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna

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Tags: Computer science, Deep learning, nVidia, nVidia GeForce GTX 1650

October 10, 2021 by hgpu

GCN Inference Acceleration using High-Level Synthesis

Yi Chien Lin, Bingyi Zhang, Viktor Prasanna

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Tags: Computer science, Data parallelism, Design space exploration, FPGA, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package

October 10, 2021 by hgpu

Implementation of Parallel Simplified Swarm Optimization in CUDA

Wei-Chang Yeh, Zhenyao Liu, Shi-Yi Tan, Shang-Ke Huang

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, Particle swarm optimization

October 10, 2021 by hgpu

HLS Portability from Intel to Xilinx: A Case Study

Zhili Xiao, Roger D. Chamberlain, Anthony M. Cabrera

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Tags: Benchmarking, Compilers, Computer science, FPGA, HLS, OpenCL, Package

October 3, 2021 by hgpu

Unified Shader Programming in C++

Kerry A. Seitz Jr., Theresa Foley, Serban D. Porumbescu, John D. Owens

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Tags: Compilers, Computer science, HLSL, nVidia, nVidia Titan RTX, Programming Languages, Programming techniques, Real-time graphics

October 3, 2021 by hgpu

Intel oneAPI DPC++ FPGA Optimization Guide

Intel Corporation

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Tags: Computer science, FPGA, Optimization, SYCL, Whitepaper

October 3, 2021 by hgpu

Embedded Software Synthesis using Heterogeneous Dataflow Models

Omair Rafique

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Tags: ATI, ATI Radeon HD 5450, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 1050, nVidia GeForce GTX 950 M, OpenCL, Thesis

October 3, 2021 by hgpu

Accelerating Encrypted Computing on Intel GPUs

Yujia Zhai, Mohannad Ibrahim, Yiqin Qiu, Fabian Boemer, Zizhong Chen, Alexey Titov, Alexander Lyashevsky

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Tags: Algorithms, Computer science, Deep learning, Intel, Package, Security, SYCL

October 3, 2021 by hgpu

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

Tsung-Wei Huang, Dian-Lun Lin, Chun-Xun Lin, Yibo Lin

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia GeForce GTX 2080, Package

September 26, 2021 by hgpu

Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing

Kamal Raj Kanakarajan, Bhuvana Kundumani, Malaikannan Sankarasubbu

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Tags: Benchmarking, Computer science, NLP

September 26, 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

Applications

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations

GCN Inference Acceleration using High-Level Synthesis

Implementation of Parallel Simplified Swarm Optimization in CUDA

HLS Portability from Intel to Xilinx: A Case Study

Unified Shader Programming in C++

Intel oneAPI DPC++ FPGA Optimization Guide

Embedded Software Synthesis using Heterogeneous Dataflow Models

Accelerating Encrypted Computing on Intel GPUs

Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System

Small-Bench NLP: Benchmark for small single GPU trained models in Natural Language Processing

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

Most viewed papers (last 30 days)