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Automatic and Portable Mapping of Data Parallel Programs to OpenCL for GPU-Based Heterogeneous Systems

https://doi.org/10.1145/2677036
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cpe.1631openalex-snapshot
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures
inferredcites →https://doi.org/10.1007/978-3-319-09967-5_5openalex-snapshot
OpenCL Task Partitioning in the Presence of GPU Contention
inferredcites →https://doi.org/10.1007/978-3-319-17473-0_10openalex-snapshot
Fast Automatic Heuristic Construction Using Active Learning
inferredcites →https://doi.org/10.1007/978-3-540-89740-8_1openalex-snapshot
CUDA-Lite: Reducing GPU Programming Complexity
inferredcites →https://doi.org/10.1007/978-3-642-11970-5_14openalex-snapshot
Automatic C-to-CUDA Code Generation for Affine Programs
inferredcites →https://doi.org/10.1007/978-3-642-19861-8_16openalex-snapshot
A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL
inferredcites →https://doi.org/10.1007/978-3-642-54807-9_9openalex-snapshot
Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code
inferredcites →https://doi.org/10.1016/j.procs.2012.04.209openalex-snapshot
CUDA: Compiling and optimizing for a GPU platform
inferredcites →https://doi.org/10.1109/cgo.2013.6494993openalex-snapshot
Portable mapping of data parallel programs to OpenCL for heterogeneous systems
inferredcites →https://doi.org/10.1109/cgo.2013.6495010openalex-snapshot
Smart, adaptive mapping of parallelism in the presence of external workload
inferredcites →https://doi.org/10.1109/hipc.2013.6799098openalex-snapshot
MaSiF: Machine learning guided auto-tuning of parallel skeletons
inferredcites →https://doi.org/10.1109/hipc.2014.7116910openalex-snapshot
Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms
inferredcites →https://doi.org/10.1109/iiswc.2011.6114174openalex-snapshot
Performance characterization of the NAS Parallel Benchmarks in OpenCL
inferredcites →https://doi.org/10.1109/inpar.2012.6339606openalex-snapshot
DL: A data layout transformation system for heterogeneous computing
inferredcites →https://doi.org/10.1109/micro.2010.44openalex-snapshot
Many-Thread Aware Prefetching Mechanisms for GPGPU Applications
inferredcites →https://doi.org/10.1109/sc.2010.36openalex-snapshot
OpenMPC: Extended OpenMP Programming and Tuning for GPUs
inferredcites →https://doi.org/10.1145/1345206.1345220openalex-snapshot
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
inferredcites →https://doi.org/10.1145/1504176.1504189openalex-snapshot
Mapping parallelism to multi-cores
inferredcites →https://doi.org/10.1145/1504176.1504194openalex-snapshot
OpenMP to GPGPU
inferredcites →https://doi.org/10.1145/1504176.1504219openalex-snapshot
Exploiting global optimizations for openmp programs in the openuh compiler
inferredcites →https://doi.org/10.1145/1542476.1542496openalex-snapshot
Towards a holistic approach to auto-parallelization
inferredcites →https://doi.org/10.1145/1669112.1669121openalex-snapshot
Qilin
inferredcites →https://doi.org/10.1145/1735688.1735697openalex-snapshot
Implementing the PGI Accelerator model
inferredcites →https://doi.org/10.1145/1735688.1735702openalex-snapshot
The Scalable Heterogeneous Computing (SHOC) benchmark suite
inferredcites →https://doi.org/10.1145/1772954.1772971openalex-snapshot
Efficient compilation of fine-grained SPMD-threaded programs for multicore CPUs
inferredcites →https://doi.org/10.1145/1806596.1806606openalex-snapshot
A GPGPU compiler for memory optimization and parallelism management
inferredcites →https://doi.org/10.1145/1815961.1816021openalex-snapshot
Debunking the 100X GPU vs. CPU myth
inferredcites →https://doi.org/10.1145/1854273.1854313openalex-snapshot
Partitioning streaming parallelism for multi-cores
inferredcites →https://doi.org/10.1145/1854273.1854336openalex-snapshot
Data layout transformation exploiting memory-level parallelism in structured grid many-core applications
inferredcites →https://doi.org/10.1145/1854273.1854340openalex-snapshot
Believe it or not!
inferredcites →https://doi.org/10.1145/1941553.1941591openalex-snapshot
Achieving a single compute device image in OpenCL for multiple GPUs
inferredcites →https://doi.org/10.1145/1944862.1944881openalex-snapshot
A workload-aware mapping approach for data-parallel programs
inferredcites →https://doi.org/10.1145/1950365.1950409openalex-snapshot
Sponge
inferredcites →https://doi.org/10.1145/1993316.1993516openalex-snapshot
Automatic CPU-GPU communication management and optimization
inferredcites →https://doi.org/10.1145/1993498.1993516openalex-snapshot
Automatic CPU-GPU communication management and optimization
inferredcites →https://doi.org/10.1145/2063384.2063401openalex-snapshot
Dymaxion
inferredcites →https://doi.org/10.1145/2145816.2145819openalex-snapshot
A performance analysis framework for identifying potential benefits in GPGPU applications
inferredcites →https://doi.org/10.1145/2168773.2168775openalex-snapshot
Parallel and Cache-Efficient In-Place Matrix Storage Format Conversion
inferredcites →https://doi.org/10.1145/2259016.2259038openalex-snapshot
Dynamically managed data for CPU-GPU architectures
inferredcites →https://doi.org/10.1145/237721.237727openalex-snapshot
Points-to analysis in almost linear time
inferredcites →https://doi.org/10.1145/2400682.2400713openalex-snapshot
Polyhedral parallel code generation for CUDA
inferredcites →https://doi.org/10.1145/2509420.2512436openalex-snapshot
Using machine learning to partition streaming programs
inferredcites →https://doi.org/10.1145/2544137.2544156openalex-snapshot
Portable and Transparent Host-Device Communication Optimization for GPGPU Environments
inferredcites →https://doi.org/10.1145/2555243.2555266openalex-snapshot
In-place transposition of rectangular matrices on accelerators
inferredcites →https://doi.org/10.1145/2579561openalex-snapshot
Integrating profile-driven parallelism detection and machine-learning-based mapping
inferredcites →https://doi.org/10.1145/2581122.2544156openalex-snapshot
Portable and Transparent Host-Device Communication Optimization for GPGPU Environments
inferredcites →https://doi.org/10.1145/258915.258943openalex-snapshot
Register promotion in C programs
inferredcites →https://doi.org/10.1145/314403.314414openalex-snapshot
Optimizing for reduced code space using genetic algorithms
inferredcites →https://doi.org/10.1145/76263.76335openalex-snapshot
A methodology for parallelizing programs for multicomputers and complex memory multiprocessors
inferredcites →https://doi.org/10.1145/996841.996853openalex-snapshot
Vectorization for SIMD architectures with alignment constraints
inferredcites →https://doi.org/10.5555/1413370.1413375openalex-snapshot
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
inferredcites →https://doi.org/10.5555/2523721.2523745openalex-snapshot
Neither more nor less: optimizing thread-level parallelism for GPGPUs

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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RALB‐HC: A resource‐aware load balancer for heterogeneous cluster
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Optimizing Sparse Matrix Multiplications for Graph Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-319-49109-7_34openalex-snapshot
Adaptive Modular Mapping to Reduce Shared Memory Bank Conflicts on GPUs
inferred← citeshttps://doi.org/10.1007/s10766-018-00625-8openalex-snapshot
Optimizing Sparse Matrix–Vector Multiplications on an ARMv8-based Many-Core Architecture
inferred← citeshttps://doi.org/10.1007/s10766-019-00646-xopenalex-snapshot
Characterizing Scalability of Sparse Matrix–Vector Multiplications on Phytium FT-2000+
inferred← citeshttps://doi.org/10.1007/s11227-017-2093-8openalex-snapshot
Enable back memory and global synchronization on LLC buffer
inferred← citeshttps://doi.org/10.1007/s11227-019-02749-1openalex-snapshot
NoT: a high-level no-threading parallel programming method for heterogeneous systems
inferred← citeshttps://doi.org/10.1007/s42514-020-00039-4openalex-snapshot
Parallel programming models for heterogeneous many-cores: a comprehensive survey
inferred← citeshttps://doi.org/10.1016/b978-0-12-804189-5.00003-xopenalex-snapshot
Controlling the design and development cycle
inferred← citeshttps://doi.org/10.1016/j.jpdc.2019.05.015openalex-snapshot
Troodon: A machine-learning based load-balancing application scheduler for CPU–GPU system
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WEEC: Web Energy Efficient Computing
inferred← citeshttps://doi.org/10.1109/access.2019.2936620openalex-snapshot
Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems
inferred← citeshttps://doi.org/10.1109/fdl50818.2020.9232934openalex-snapshot
Machine Learning in Compilers: Past, Present and Future
inferred← citeshttps://doi.org/10.1109/hpcc/smartcity/dss.2018.00116openalex-snapshot
Adaptive Optimization of Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures
inferred← citeshttps://doi.org/10.1109/hpcc/smartcity/dss.2019.00101openalex-snapshot
Auto-Tuning MPI Collective Operations on Large-Scale Parallel Systems
inferred← citeshttps://doi.org/10.1109/infocom.2017.8057087openalex-snapshot
Optimise web browsing on heterogeneous mobile platforms: A machine learning based approach
inferred← citeshttps://doi.org/10.1109/infocom41043.2020.9155489openalex-snapshot
Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions
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Auto-tuning Streamed Applications on Intel Xeon Phi
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ChipAdvisor: A Machine Learning Approach for Mapping Applications to Heterogeneous Systems
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Machine Learning in Compiler Optimization
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The Approximate Discrete Radon Transform: A Case Study in Auto-Tuning of OpenCL Implementations
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Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels
inferred← citeshttps://doi.org/10.1109/tfuzz.2022.3167158openalex-snapshot
Fuzzy Active Learning to Detect OpenCL Kernel Heterogeneous Machines in Cyber Physical Systems
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Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures
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CID: A Compile-Time Implementation Decider for Heterogeneous Platforms Based on C++ Attributes
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Lost in heterogeneity
inferred← citeshttps://doi.org/10.1145/3078633.3081040openalex-snapshot
Adaptive optimization for OpenCL programs on embedded heterogeneous systems
inferred← citeshttps://doi.org/10.1145/3135974.3135984openalex-snapshot
Improving spark application throughput via memory aware task co-location
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Adaptive optimization for OpenCL programs on embedded heterogeneous systems
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MOCL
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Adaptive deep learning model selection on embedded systems
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A portable, automatic data qantizer for deep neural networks
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Proteus
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Adaptive deep learning model selection on embedded systems
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Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection
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Automated conformance testing for JavaScript engines via deep compiler fuzzing
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LIBSHALOM
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CoopCL
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VEGA: Automatically Generating Compiler Backends using a Pre-trained Transformer Model
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BePilot: An AI Programming Assistant for Compiler Backend Development
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Improving Spark Application Throughput Via Memory Aware Task Co-location: A Mixture of Experts Approach
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Energy-aware Web Browsing on Heterogeneous Mobile Platforms
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Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach
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Machine Learning in Compiler Optimisation
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Adaptive Selection of Deep Learning Models on Embedded Systems
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Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures
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Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Multi-cores
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Smart, Adaptive Energy Optimization for Mobile Web Interactions
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Parallel Programming Models for Heterogeneous Many-Cores : A Survey
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