An Efficient GPU Implementation of Inclusion-Based Pointer Analysis

We present an efficient GPU implementation of Andersen's whole-program inclusion-based pointer analysis, a fundamental analysis on which many others are based, including optimising compilers, bug detection and security analyses. Andersen's algorithm makes extensive modifications to the gra...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 27; no. 2; pp. 353 - 366
Main Authors: Su, Yu, Ye, Ding, Xue, Jingling, Liao, Xiang-Ke
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Algorithm design and analysis
Algorithms
Balancing
Benchmarks
compilers
Computer information security
GPGPU
Graphics processing units
Graphs
Instruction sets
Optimization
Parallel graph algorithms
Partitioning algorithms
pointer analysis
Switches
Synchronization
Vectors
Workload
pointer analysis
compilers
GPGPU
Parallel graph algorithms
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:We present an efficient GPU implementation of Andersen's whole-program inclusion-based pointer analysis, a fundamental analysis on which many others are based, including optimising compilers, bug detection and security analyses. Andersen's algorithm makes extensive modifications to the graph that represents the pointer-manipulating statements in a program. These modifications are highly irregular, input-dependent and statically unpredictable, making it much more challenging to balance such graph workloads across a multitude of GPU cores than those dealt with by traditional graph algorithms such as DFS and BFS. To parallelise Andersen's analysis efficiently on GPUs, we introduce an imbalance-aware workload partitioning scheme that divides its workload dynamically among the concurrent warps, initially in a warp-centric manner (during the coarsegrain stage) but later switches to a task-pool-based model when a workload imbalance is detected (during the fine-grain stage). We improve further its performance by using an adaptive group propagation scheme to reduce some redundant traversals. For a set of 14 C benchmarks evaluated, our parallel implementation of Andersen's analysis achieves a significant speedup of 46 percent on average over the state-of-the art on an NVIDIA Tesla K20c GPU.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2015.2397933