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hgpu.org » Gravitational lensing

Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor

Bin Chen, Ronald Kantowski, Xinyu Dai, Eddie Baron, Paul Van der Mark

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Tags: Astrophysics, CUDA, Gravitation, Gravitational lensing, Intel Xeon Phi, nVidia, OpenMP, Tesla M2050

May 2, 2017 by hgpu

Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters

Christopher J. Fluke, David G. Barnes, Benjamin R. Barsdell, Amr H. Hassan

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Tags: Astrophysics, ATI, ATI Radeon HD 5970, CUDA, Gravitational lensing, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce 8800 GT, OpenCL, Tesla C1060

November 10, 2010 by hgpu

Teraflop per second gravitational lensing ray-shooting using graphics processing units

Alexander C. Thompson, Christopher J. Fluke, David G. Barnes, Benjamin R. Barsdell

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Tags: Astrophysics, Cosmology and Extragalactic Astrophysics, CUDA, Gravitational lensing, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce 8800 GT, Tesla S1070

November 9, 2010 by hgpu

Computational advances in gravitational microlensing: a comparison of CPU, GPU, and parallel, large data codes

N. F. Bate, C. J. Fluke, B. R. Barsdell, H. Garsden, G. F. Lewis

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Tags: Astrophysics, CUDA, Gravitational lensing, Instrumentation and Methods for Astrophysics, nVidia, Tesla S1070

October 27, 2010 by hgpu

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

AgentGarten: Code Worlds for Evolving Agents

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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high performance computing on graphics processing units: hgpu.org

Accelerating gravitational microlensing simulations using the Xeon Phi coprocessor

Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters

Teraflop per second gravitational lensing ray-shooting using graphics processing units

Computational advances in gravitational microlensing: a comparison of CPU, GPU, and parallel, large data codes

Recent source codes

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

AgentGarten: Code Worlds for Evolving Agents

Brian2 Atlas

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

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