Applications

hgpu.org » Applications

Deep Learning For Smile Recognition

Patrick O. Glauner

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

February 3, 2016 by hgpu

Edge coloring in unstructured CFD codes

Andrew Giuliani, Lilia Krivodonova

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Tags: Algorithms, Computer science, Fluid dynamics, nVidia, nVidia GeForce GTX 580

February 3, 2016 by hgpu

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Ashwin Trikuta Srinath

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Tags: Algorithms, Computer science, CUDA, Finite difference, Fluid dynamics, GPU cluster, Numerical simulation, nVidia, OpenMPI, Tesla K20, Tesla K40, Thesis

February 3, 2016 by hgpu

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Flavio Vella, Giancarlo Carbone, Massimo Bernaschi

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Graph theory, nVidia, OpenMPI, Tesla K20, Tesla K80

February 3, 2016 by hgpu

Fast, Realistic Terrain Synthesis

Justin Crause

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 660 Ti, Thesis

February 2, 2016 by hgpu

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Prateek Tandon

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Tags: ASIC, big data, Computer science, FPGA, Hadoop, hardware acceleration, Machine learning, MapReduce, NLP, Thesis

February 2, 2016 by hgpu

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Yifei Li

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Source codes

Tags: Algorithms, Clustering, Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, OpenMP, Package, Thesis, Visualization

February 2, 2016 by hgpu

Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders

Giuseppe Cerati, Peter Elmer, Steven Lantz, Kevin McDermott, Dan Riley, Matevz Tadel, Peter Wittich, Frank Wurthwein, Avi Yagil

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Tags: High Energy Physics - Experiment, Instrumentation and Detectors, Intel Xeon Phi, Physics

February 2, 2016 by hgpu

GPGPU Multi Object Bayesian Tracking with an embedded System on a Chip

David Webb

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Tags: Bayesian, Computer science, Computer vision, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 960, nVidia Jetson TK1, SoC, Thesis

February 2, 2016 by hgpu

A power-aware symbiotic scheduling algorithm for concurrent GPU kernels

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Task scheduling, Tesla K20

February 1, 2016 by hgpu

Accelerating the Critical Line Algorithm for Portfolio Optimization Using GPUs

Raja H. Singh, Lee Barford, Frederick Harris Jr.

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Tags: Algorithms, Computer science, CUDA, Finance, nVidia, nVidia GeForce GTX 780

February 1, 2016 by hgpu

Deep learning review and its applications

Andoni Azkarate Saiz

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 660, Review, Thesis

February 1, 2016 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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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high performance computing on graphics processing units: hgpu.org

Applications

Deep Learning For Smile Recognition

Edge coloring in unstructured CFD codes

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Fast, Realistic Terrain Synthesis

Hardware Acceleration for Unstructured Big Data and Natural Language Processing

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Kalman-Filter-Based Particle Tracking on Parallel Architectures at Hadron Colliders

GPGPU Multi Object Bayesian Tracking with an embedded System on a Chip

A power-aware symbiotic scheduling algorithm for concurrent GPU kernels

Accelerating the Critical Line Algorithm for Portfolio Optimization Using GPUs

Deep learning review and its applications

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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

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

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