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Data Partitioning on Heterogeneous Multicore and Multi-GPU Systems Using Functional Performance Models of Data-Parallel Applications
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A new parallel matrix multiplication algorithm on distributed-memory concurrent computers
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Automatic Calibration of Performance Models on Heterogeneous Multicore Architectures
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Column-Based Matrix Partitioning for Parallel Matrix Multiplication on Heterogeneous Processors Based on Functional Performance Models
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The FLAME approach: From dense linear algebra algorithms to high-performance multi-accelerator implementations
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Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing
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Data Partitioning on Heterogeneous Multicore Platforms
inferredcites →https://doi.org/10.1109/hpc.1997.592151openalex-snapshot
A new parallel matrix multiplication algorithm on distributed-memory concurrent computers
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Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs
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An efficient, model-based CPU-GPU heterogeneous FFT library
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Evaluation and tuning of the Level 3 CUBLAS for graphics processors
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DYNAMIC LOAD BALANCING OF PARALLEL COMPUTATIONAL ITERATIVE ROUTINES ON HIGHLY HETEROGENEOUS HPC PLATFORMS
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Solving dense linear systems on platforms with multiple hardware accelerators
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Accelerating linpack with CUDA on heterogenous clusters
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Solving dense linear systems on platforms with multiple hardware accelerators
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Enabling and scaling matrix computations on heterogeneous multi-core and multi-GPU systems
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Data Partitioning with a Functional Performance Model of Heterogeneous Processors
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Finite-Difference in Time-Domain Scalable Implementations on CUDA and OpenCL
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FuPerMod: A Framework for Optimal Data Partitioning for Parallel Scientific Applications on Dedicated Heterogeneous HPC Platforms
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Analysis on Heterogeneous Computing
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A Proposed Data Partitioning Approach on Heterogeneous HPC Platforms: Data Locality Perspective
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OpenH: A Novel Programming Model and API for Developing Portable Parallel Programs on Heterogeneous Hybrid Servers
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Global hybrid multi-core-GPUs-resources platform (GHRP) in hard real time system
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A Unified Programming Model for Heterogeneous Computing with CPU and Accelerator Technologies
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PK-OMLP: An OMLP Based k-Exclusion Real-Time Locking Protocol for Multi-GPU Sharing under Partitioned Scheduling
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Performance Portability of Generated Cardiac Simulation Kernels Through Automatic Dimensioning and Load Balancing on Heterogeneous Nodes
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Dynamic Fine-Grained Workload Partitioning for Irregular Applications on Discrete CPU-GPU Systems
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Data Partitioning on Multicore and Multi-GPU Platforms Using Functional Performance Models
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Model-Based Estimation of the Communication Cost of Hybrid Data-Parallel Applications on Heterogeneous Clusters
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Advanced Trends of Heterogeneous Computing with CPU-GPU Integration: Comparative Study
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Current trends in heterogeneous systems: A review
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STUDYING THE OPTIMAL FORM OF PARTITIONING THE DATA FOR THE MATRIX MULTIPLICATION ON THREE FULLY CONNECTED HETEROGENEOUS PROCESSORS WITH DIFFERENT BANDWIDTHS
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