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An Accurate GPU Performance Model for Effective Control Flow Divergence Optimization

https://doi.org/10.1109/ipdps.2012.18
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What this work says

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

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Sequence alignment with GPU: Performance and design challenges
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Analyzing CUDA workloads using a detailed GPU simulator
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Demystifying GPU microarchitecture through microbenchmarking
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Microarchitectural Design Space Exploration Using an Architecture-Centric Approach
inferredcites →https://doi.org/10.1109/micro.2007.30openalex-snapshot
Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow
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Basic block distribution analysis to find periodic behavior and simulation points in applications
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An efficient k-means clustering algorithm: analysis and implementation
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Dynamic warp formation
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An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
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An adaptive performance modeling tool for GPU architectures
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Streamlining GPU applications on the fly
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Dynamic warp subdivision for integrated branch and memory divergence tolerance
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On-the-fly elimination of dynamic irregularities for GPU computing
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Marching cubes: A high resolution 3D surface construction algorithm
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Determining average program execution times and their variance
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Real-time implementation and performance optimization of 3D sound localization on GPUs

What points at this work

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

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A Throughput-Aware Analytical Performance Model for GPU Applications
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Novel Approach: Deduplication for Backup Systems Using Data Block Size
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Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment
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G-TSC: Timestamp Based Coherence for GPUs
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A quantitative study of irregular programs on GPUs
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Extendable framework for monitoring heterogeneous multi-accelerator HPC cluster
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Static WCET Analysis of GPUs with Predictable Warp Scheduling
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CRAT: Enabling Coordinated Register Allocation and Thread-Level Parallelism Optimization for GPUs
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An Efficient Compiler Framework for Cache Bypassing on GPUs
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An Accurate GPU Performance Model for Effective Control Flow Divergence Optimization
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A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing
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Efficient GPU Spatial-Temporal Multitasking
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Energy-Efficient Resource Management for Federated Edge Learning With CPU-GPU Heterogeneous Computing
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Enabling coordinated register allocation and thread-level parallelism optimization for GPUs
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CUDAAdvisor: LLVM-based runtime profiling for modern GPUs
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On-GPU Thread-Data Remapping for Branch Divergence Reduction
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Investigation of the Effectiveness of Programs Optimization Methods for Parallel Computing Systems with GPU
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A SHARED MEMORY BASED IMPLEMENTATION OF NEEDLEMAN-WUNSCH ALGORITHM USING SKEWING TRANSFORMATION
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C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
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FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
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