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RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

Ingo Wald, Will Usher, Nate Morrical, Laura Lediaev, Valerio Pascucci

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Tags: 3D Graphics and Realism, CUDA, nVidia, nVidia Titan RTX, Raytracing

June 16, 2019 by hgpu

SYCL Code Generation for Multigrid Methods

Stefan Groth, Christian Schmitt, Jürgen Teich, and Frank Hannig

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Tags: Code generation, Computer science, Differential equations, OpenCL, Package, Partial differential equations, PDEs

June 16, 2019 by hgpu

Software Compilation Techniques for Heterogeneous Embedded Multi-Core Systems

Rainer Leupers, Miguel Angel Aguilar, Jeronimo Castrillon, Weihua Sheng

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Tags: ARM, Code generation, Compilers, Computer science, DSP, Heterogeneous systems

June 16, 2019 by hgpu

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Viktor Rosenfeld, Sebastian Bress, Steffen Zeuch, Tilmann Rabl, Volker Markl

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Hashing, nVidia, nVidia GeForce GXT 1080, nVidia GeForce GXT 980, OpenCL, Performance, Tesla K40, Tesla V100

June 16, 2019 by hgpu

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Andre Viebke, Sabri Pllana, Suejb Memeti, Joanna Kolodziej

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Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Performance

June 12, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

Sparsh Mittal, Osho Anand, Visnu P Kumarr

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Tags: Compilers, Computer science, Intel Xeon Phi, OpenMP, survey

June 9, 2019 by hgpu

PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion

Ying Liu, Lei Huang, Mingchuan Wu, Huimin Cui, Fang Lv, Xiaobing Feng, Jingling Xue

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Tags: Code generation, Compilers, Computer science, OpenCL

June 9, 2019 by hgpu

Diagnosing Performance Bottlenecks in HPC Applications

Kenneth Czechowski

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Tags: Computer science, HPC, Package, Performance, Thesis

June 9, 2019 by hgpu

LAMDA: Learning-Assisted Multi-Stage Autotuning for FPGA Design Closure

Ecenur Ustun, Shaojie Xiang, Jinny Gui, Cunxi Yu, Zhiru Zhang

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Tags: Computer science, FPGA, OpenCL, Performance

June 9, 2019 by hgpu

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Elias Stehle, Hans-Arno Jacobsen

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, nVidia, nVidia GeForce GTX Titan X

June 5, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

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

June 5, 2019 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Applications

RTX Beyond Ray Tracing: Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location

SYCL Code Generation for Multigrid Methods

Software Compilation Techniques for Heterogeneous Embedded Multi-Core Systems

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

A Survey on Evaluating and Optimizing Performance of Intel Xeon Phi

PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion

Diagnosing Performance Bottlenecks in HPC Applications

LAMDA: Learning-Assisted Multi-Stage Autotuning for FPGA Design Closure

ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data

Dynamic Distribution Pruning for Efficient Network Architecture Search

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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