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Challenges Adapting CUDA PIC Codes to multiple GPUs

Olav Emil Eiksund

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Tags: Benchmarking, CUDA, Electrostatics, FFT, nVidia, nVidia GeForce GTX 980, OpenMP, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Tesla K20, Thesis

December 19, 2015 by hgpu

Efficient Query Processing in Co-Processor-accelerated Databases

Sebastian Bress

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Tags: Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 640, Package, Tesla C1060, Tesla C2050, Thesis

December 19, 2015 by hgpu

A CUDA Kernel Scheduler Exploiting Static Data Dependencies

Eva Burrows

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Tags: Computer science, CUDA, nVidia, Task scheduler

December 15, 2015 by hgpu

Run-time support for multi-level disjoint memory address spaces

Javier Bueno Hedo

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Tags: Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 480, OmpSs, OpenMP, Thesis

December 15, 2015 by hgpu

Compressed Dynamic Mode Decomposition for Real-Time Object Detection

N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 950 M, Sensing

December 15, 2015 by hgpu

MapGraph: A High Level API for Fast Development of High Performance Graph Analytics on GPUs

Zhisong Fu, Michael Personick, Bryan Thompson

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Package, Tesla K20

December 10, 2015 by hgpu

Transforming C OpenMP Programs for Verification in CIVL

Michael Rogers

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Tags: Computer science, CUDA, MPI, nVidia, OpenCL, OpenMP, Pthreads, Thesis

December 10, 2015 by hgpu

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, Zhenyao Zhu

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro K 1200, Package, Speech recognition

December 10, 2015 by hgpu

High Performance Histograms on SIMT and SIMD Architectures

M.E.R. Berger

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Tags: AMD Radeon R9 290X, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 660, nVidia GeForce GTX 750, OpenCL, Performance, Thesis

December 10, 2015 by hgpu

Join Execution Using Fragmented Columnar Indices on GPU and MIC

Elena V. Ivanova, Stepan O. Prikazchikov, Leonid B. Sokolinsky

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Tags: Computer science, CUDA, Databases, Intel Xeon Phi, nVidia, Tesla K40

December 10, 2015 by hgpu

A Parallel Solver for Markov Decision Process in Crowd Simulations

Sergio Ruiz, Benjamin Hernandez

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Tags: ARM, Computer science, Crowd simulation, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia Jetson TK1, Path problems, Tesla K40, Thrust

December 9, 2015 by hgpu

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 980, Package

December 8, 2015 by hgpu

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

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

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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

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

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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