Heterogeneous systems | Page 64

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Towards Porting a Real-World Seismological Application to the Intel MIC Architecture

V. Weinberg, M. Allalen, G. Brietzke

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Tags: Earth and Space Sciences, Fortran, Geoscience, Heterogeneous systems, Intel Xeon Phi, MPI, Seismology

February 16, 2014 by hgpu

Designing Bit-Reproducible Portable High-Performance Applications

Andrea Arteaga, Oliver Fuhrer, Torsten Hoefler

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Portability, Tesla K20

February 12, 2014 by hgpu

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Tarun Beri, Sorav Bansal, Subodh Kumar

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Tags: Computer science, CUDA, FFT, GPU cluster, Heterogeneous systems, Matrix multiplication, Memory model, nVidia, Prefetch, Task scheduling, Tesla M2070

February 11, 2014 by hgpu

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Vivek K. Pallipuram

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Performance, Tesla K20, Tesla M2075, Thesis

February 11, 2014 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 5, 2014 by hgpu

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Angeles Navarro, Antonio Vilches, Francisco Corbera, Rafael Asenjo

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla S2050

February 4, 2014 by hgpu

Task migration of DSP application specified with a DFG and implemented with the BSP computing model on a CPU-GPU cluster

Farouk Mansouri, Sylvain Huet, Vincent Fristot, Dominique Houzet

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Tags: Computer science, CUDA, DSP, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia Quadro 4000, Signal processing

February 2, 2014 by hgpu

Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

Xavier Lacoste, Mathieu Faverge, Pierre Ramet, Samuel Thibault, George Bosilca

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, nVidia, Sparse direct solvers, Tesla M2070

January 30, 2014 by hgpu

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

Gary Macindoe

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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

Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems

Jing Zhang, Hao Wang, Heshan Lin, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Task scheduling

January 29, 2014 by hgpu

On the Programmability and Performance of Heterogeneous Platforms

Konstantinos Krommydas, Thomas R.W. Scogland, Wu-chun Feng

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, N-body simulation, nVidia, OpenACC, Optimization, Tesla K20

January 29, 2014 by hgpu

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Ashis Kumar Dash

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Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Quadro FX 3700

January 29, 2014 by hgpu

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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

Towards Porting a Real-World Seismological Application to the Intel MIC Architecture

Designing Bit-Reproducible Portable High-Performance Applications

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Strategies for Maximizing Utilization in multi-CPU & multi-GPU Heterogeneous Architectures

Task migration of DSP application specified with a DFG and implemented with the BSP computing model on a CPU-GPU cluster

Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes

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

Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems

On the Programmability and Performance of Heterogeneous Platforms

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Recent source codes

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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

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

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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