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hlslib: Software Engineering for Hardware Design

Johannes de Fine Licht, Torsten Hoefler

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Tags: Computer science, FPGA, OpenCL, Package, Software Engineering

October 13, 2019 by hgpu

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Performance, Sparse, Tesla P100, Tesla V100

October 13, 2019 by hgpu

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, Frédo Durand

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Tags: 3D Graphics and Realism, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 1080 Ti, Package, Rendering, Tesla V100

October 6, 2019 by hgpu

waLBerla: A block-structured high-performance framework for multiphysics simulations

Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde

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Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100

October 6, 2019 by hgpu

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Maria Papadaki, Yannis Sfakianakis, Christos Kozanitis, Angelos Bilas

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Tags: Computer science, CUDA, GPU cluster, Grid, nVidia, nVidia Quadro K 2200

October 6, 2019 by hgpu

MIOpen: An Open Source Library For Deep Learning Primitives

Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package

October 6, 2019 by hgpu

Heterogeneous Resource-Elastic Management for FPGAs: Concepts, Theory and Implementation

Anuj Vaishnav, Dirk Koch, Khoa Dang Pham

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, OpenCL

September 29, 2019 by hgpu

Futhark Vulkan Backend

Steffen Holst Larsen

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Tags: Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, Vulkan

September 29, 2019 by hgpu

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Abhinav Jangda, Arjun Guha

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Tags: CUDA, Image procesing, nVidia, nVidia GeForce GTX 1080 Ti, Python, Tesla V100

September 22, 2019 by hgpu

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Dejice Jacob, Jeremy Singer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 1060, Python

September 22, 2019 by hgpu

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Speech recognition

September 22, 2019 by hgpu

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

Natalija Stojanovic, Dragan Stojanovic

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Tags: Algorithms, Computer science, CUDA, nVidia, OpenACC, Package, Tesla K80

September 15, 2019 by hgpu

FP4 FlashAttention forward, backward, and TorchTitan reproduction code

Hardware-Aware FP4 FlashAttention-4

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

PortLBM: A Portable Lattice Boltzmann Tool Leveraging SYCL on AMD, NVIDIA, and Intel GPUs

Harness Engineering for LLM-Driven GPU Kernel Generation

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

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