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C-DAC’s Efforts – Application Kernels on HPC Cluster with GPU Accelerators

VCV.Rao, Nisha Agrawa, Samrit Maity

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Tags: AMD FirePro V5900, AMD FirePro V7900, AMD FireStream 9350, ATI, Computer science, CUDA, Differential equations, FEM, Finite difference, Finite element method, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, OpenCL, Partial differential equations, PDEs, Tesla C2060

May 19, 2012 by hgpu

High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

Jeswin Godwin, Justin Holewinski, P. Sadayappan

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Tags: Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla C2050

April 25, 2012 by hgpu

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

Takayuki Muranushi

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Tags: Algorithms, Astrophysics, CUDA, Differential equations, Instrumentation and Methods for Astrophysics, nVidia, Package, Partial differential equations, PDEs, Tesla M2050

April 25, 2012 by hgpu

Impact of asynchronism on GPU accelerated parallel iterative computations

Sylvain Contassot-Vivier, Thomas Jost, Stephane Vialle

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Partial differential equations, PDEs, Performance

March 22, 2012 by hgpu

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dimitar Lukarski

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FEM, Finite difference, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla M1060, Tesla S1070, Thesis

March 16, 2012 by hgpu

Multi-platform Linear Algebra

E. Ketelaer, D. Lukarski

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Tags: Computer science, CUDA, Differential equations, Distributed data structures, Linear Algebra, nVidia, Package, Partial differential equations, PDEs

March 16, 2012 by hgpu

Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods

Tomas Oberhuber, Atsushi Suzuki, Jan Vacata, Vitezslav Zabka

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Tags: Algorithms, CUDA, Differential equations, Image processing, MRI, nVidia, nVidia GeForce GTX 480, ODEs, Ordinary differential equations, Partial differential equations, PDEs

February 22, 2012 by hgpu

Parallel Semi-Implicit Time Integrators

B. Ong, A. Melfi, A. Christlieb

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Partial differential equations, PDEs, Tesla C1060

February 9, 2012 by hgpu

Scientific Computation on Graphics Processing Unit using CUDA

Pradip Narendrakumar Panchal

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Tags: Computer science, CUDA, Differential equations, FDTD, Finite difference, Finite-difference time-domain, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs, Thesis

January 23, 2012 by hgpu

Numerical solution of PDEs with hybrid and heterogeneous computing models

Markus Hegland

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Tags: Algorithms, CUDA, Differential equations, Fluid dynamics, Heterogeneous systems, nVidia, nVidia GeForce GTX 295, Partial differential equations, PDEs, Physics, Plasma physics, Review

December 23, 2011 by hgpu

Multi-Science Applications with Single Codebase – GAMER – for Massively Parallel Architectures

Hemant Shukla, Hsi-Yu Schive, Tak-Pong Woo, Tzihong Chiueh

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Tags: Algorithms, Astrophysics, Benchmarking, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Physics, Rendering, Tesla C2050

December 10, 2011 by hgpu

Playdoh: A lightweight Python library for distributed computing and optimisation

Cyrille Rossant, Bertrand Fontaine, Dan F.M. Goodman

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Tags: Computer science, CUDA, Differential equations, Distributed computing, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Package, Partial differential equations, PDEs, Python

December 10, 2011 by hgpu

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

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

CANNBot Skills

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

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high performance computing on graphics processing units: hgpu.org

C-DAC’s Efforts – Application Kernels on HPC Cluster with GPU Accelerators

High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

Impact of asynchronism on GPU accelerated parallel iterative computations

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Multi-platform Linear Algebra

Image segmentation using CUDA implementations of the Runge-Kutta-Merson and GMRES methods

Parallel Semi-Implicit Time Integrators

Scientific Computation on Graphics Processing Unit using CUDA

Numerical solution of PDEs with hybrid and heterogeneous computing models

Multi-Science Applications with Single Codebase – GAMER – for Massively Parallel Architectures

Playdoh: A lightweight Python library for distributed computing and optimisation

Recent source codes

FP4 FlashAttention forward, backward, and TorchTitan reproduction code

EinsummableV3 - graph-based computation with tensor operations support

MaxKernel: Intelligent TPU Kernel Engineering Agents

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

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

TensorCorePTX: PTX Tensor Core GEMM Exploration

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

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

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