CGO 2024
Sat 2 - Wed 6 March 2024 Edinburgh, United Kingdom
Tue 5 Mar 2024 14:20 - 14:40 at Tinto - Static/Dynamic Analyses Chair(s): Laure Gonnord

Soft errors are a type of transient digital signal corruption that occurs in digital hardware components such as the internal flip-flops of CPU pipelines, the register file, memory cells, and even internal communication buses. Soft errors are caused by environmental radioactivity, magnetic interference, lasers, and temperature fluctuations, either unintentionally, or as part of a deliberate attempt to compromise a system and expose confidential data.

We propose a bit-level error coalescing (BEC) static program analysis and its two use cases to understand and improve program reliability against soft errors. The BEC analysis tracks each bit corruption in the register file and classifies the effect of the corruption by its semantics at compile time. The usefulness of the proposed analysis is demonstrated in two scenarios, fault injection campaign pruning, and reliability-aware program transformation. Experimental results show that bit-level analysis pruned up to 30.04 % of exhaustive fault injection campaigns (13.71 % on average), without loss of accuracy. Program vulnerability was reduced by up to 13.11 % (4.94 % on average) through bit-level vulnerability-aware instruction scheduling. The analysis has been implemented within LLVM and evaluated on the RISC-V architecture.

To the best of our knowledge, the proposed BEC analysis is the first bit-level compiler analysis for program reliability against soft errors. The proposed method is generic and not limited to a
specific computer architecture.

Tue 5 Mar

Displayed time zone: London change

14:20 - 15:40
Static/Dynamic AnalysesMain Conference at Tinto
Chair(s): Laure Gonnord Univ. Grenoble Alpes, Grenoble INP, LCIS, Valence, France
14:20
20m
Talk
BEC: Bit-Level Static Analysis for Reliability against Soft Errors
Main Conference
Yousun Ko Yonsei University, Bernd Burgstaller Yonsei University
Pre-print
14:40
20m
Talk
Boosting the Performance of Multi-solver IFDS Algorithms with Flow-Sensitivity Optimizations
Main Conference
Haofeng Li Institute of Computing Technology at Chinese Academy of Sciences, Jie Lu Institute of Computing Technology at Chinese Academy of Sciences, Haining Meng Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Liqing Cao Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Lian Li Institute of Computing Technology at Chinese Academy of Sciences; University of Chinese Academy of Sciences; Zhongguancun Laboratory, Lin Gao TianqiSoft
Pre-print
15:00
20m
Talk
Representing Data Collections in an SSA Form
Main Conference
Tommy McMichen Northwestern University, Nathan Greiner Northwestern University, Peter Zhong Northwestern University, Federico Sossai Northwestern University, Atmn Patel Northwestern University, Simone Campanoni Northwestern University
Pre-print
15:20
20m
Talk
Revamping Sampling-Based PGO with Context-Sensitivity and Pseudo-instrumentation
Main Conference
Wenlei He Meta, Hongtao Yu Meta, Lei Wang Meta, Taewook Oh Meta