Machine learning | Page 53

hgpu.org » Machine learning

Automatic Scheduling of Compute Kernels Across Heterogeneous Architectures

Robert F. Lyerly

View

Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning, nVidia, OpenCL, OpenMP, Task scheduling, Tesla C2075

May 9, 2014 by hgpu

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss, Vijay S. Pande

View

Download (PDF)

Tags: Algorithms, Biology, Biophysics, CUDA, Distributed computing, Machine learning, Molecular dynamics, nVidia, nVidia GeForce GTX Titan

May 9, 2014 by hgpu

One weird trick for parallelizing convolutional neural networks

Alex Krizhevsky

View

Download (PDF)

Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, Tesla K20

April 25, 2014 by hgpu

Measuring the evolving Internet ecosystem with exchange points

Mohammad Zubair Ahmad

View

Download (PDF)

Tags: Computer science, CUDA, Machine learning, Networks, nVidia, nVidia GeForce 9400, nVidia GeForce GTX 260 M, Thesis

April 22, 2014 by hgpu

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

View

Download (PDF)

Source codes

Tags: Bayesian, Biology, CUDA, Machine learning, Neural networks, nVidia, Package, Python

April 17, 2014 by hgpu

Exploring the power of GPU’s for training Deep Belief Networks

Vivek Kulkarni

View

Download (PDF)

Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 570

April 9, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

View

Download (PDF)

Source codes

Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

Parallel Spectral Graph Partitioning on CUDA

Orhan Firat, Alptekin Temizel

View

Download (PDF)

Tags: Algorithms, CUDA, Image processing, Machine learning, nVidia, Tesla C2070

February 23, 2014 by hgpu

Fast American Basket Option Pricing on a multi-GPU Cluster

Michael Benguigui, Francoise Baude

View

Download (PDF)

Tags: Finance, GPU cluster, Machine learning, nVidia, OpenCL, Option pricing, Tesla M2050, Tesla M2075, Tesla S1070

February 17, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

View

Download (PDF)

Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

View

Download (PDF)

Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Automatic Scheduling of Compute Kernels Across Heterogeneous Architectures

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

One weird trick for parallelizing convolutional neural networks

Measuring the evolving Internet ecosystem with exchange points

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Exploring the power of GPU’s for training Deep Belief Networks

Parallel Support Vector Machines in Practice

Parallel Spectral Graph Partitioning on CUDA

Fast American Basket Option Pricing on a multi-GPU Cluster

Implementing the Projected Spatial Rich Features on a GPU

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

LLM.Q: Quantized LLM training in pure CUDA/C++

Most viewed papers (last 30 days)