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Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Pietro Ghiglio, Uwe Dolinsky, Mehdi Goli, Kumudha Narasimhan

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, performance portability, SYCL

May 1, 2022 by hgpu

Optimizing Performance and Energy Efficiency in Massively Parallel Systems

Raúl Nozal González

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Tags: AMD Radeon RX 5700 XT, ATI, Computer science, Heterogeneous systems, HPC, nVidia, nVidia GeForce GTX 950, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, SYCL, Tesla K20, Thesis

April 10, 2022 by hgpu

Extending SYCL’s Programming Paradigm with Tensor-based SIMD Abstractions

Wilson Feng, Shucai Yao, Kai Ting Wang, Md Aamir Raihan, Laichun Feng, Chunrong Xu

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Tags: Benchmarking, Computer science, Heterogeneous systems, Matrix multiplication, SYCL

April 10, 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

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

Vincent R. Pascuzzi, Mehdi Goli

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, FFT, Heterogeneous systems, HIP, HPC, nVidia, nVidia A100, OpenCL, performance portability, SYCL

March 20, 2022 by hgpu

Multi-hetero Acceleration by GPU and FPGA for Astrophysics Simulation on oneAPI Environment

Ryuta Kashino, Ryohei Kobayashi, Norihisa Fujita, Taisuke Boku

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Tags: Astrophysics, CUDA, FPGA, Heterogeneous systems, nVidia, OpenCL, Physics, SYCL

January 23, 2022 by hgpu

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Dengpan Wang

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Operating systems, PTX, SYCL, Thesis

January 16, 2022 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

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

An Experimental Study of SYCL Task Graph Parallelism for Large-Scale Machine Learning Workloads

Cheng-Hsiang Chiu, Dian-Lun Lin, Tsung-Wei Huang

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

September 26, 2021 by hgpu

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

Vincent R. Pascuzzi, Mehdi Goli

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Tags: AMD Radeon RX Vega 56, ATI, Computer science, Heterogeneous systems, nVidia, nVidia A100, performance portability, Pseudo-random number generators, SYCL

September 19, 2021 by hgpu

neoSYCL: a SYCL implementation for SX-Aurora TSUBASA

Yinan Ke, Mulya Agung, Hiroyuki Takizawa

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Tags: Benchmarking, Computer science, Heterogeneous systems, LLVM, SYCL

August 29, 2021 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Improving performance of SYCL applications on CPU architectures using LLVM-directed compilation flow

Optimizing Performance and Energy Efficiency in Massively Parallel Systems

Extending SYCL’s Programming Paradigm with Tensor-based SIMD Abstractions

Migrating CUDA to oneAPI: A Smith-Waterman Case Study

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

Multi-hetero Acceleration by GPU and FPGA for Astrophysics Simulation on oneAPI Environment

Research and Development of Porting SYCL on QNX Operating System for High Parallelism

Intel oneAPI DPC++ FPGA Optimization Guide

Accelerating Encrypted Computing on Intel GPUs

An Experimental Study of SYCL Task Graph Parallelism for Large-Scale Machine Learning Workloads

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

neoSYCL: a SYCL implementation for SX-Aurora TSUBASA

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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