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Dynamic Data-Race Detection through the Fine-Grained Lens
[article]
2021
arXiv
pre-print
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In this work we establish several fine-grained separations between many popular notions of dynamic data races. The input is an execution trace with N events, T threads and L locks. ...
The standard approach to data-race detection is via dynamic analyses, which work over executions of concurrent programs, instead of the program source code. ...
Well-
23:6 Dynamic Data-Race Detection through the Fine-Grained Lens formedness then entails that for each lock ℓ, the projection σ| ℓ is a prefix of some string in the language of the grammar with production ...
arXiv:2107.03569v1
fatcat:gihpfdzmyncw3dsv6v67zbfo7e
Infrastructure-Free Logging and Replay of Concurrent Execution on Multiple Cores
[chapter]
2014
Lecture Notes in Computer Science
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During replay, our technique first tries to follow only the event order in the log. However, due to schedule differences, replay may fail. ...
The exploration is facilitated by a sophisticated caching mechanism. ...
Any opinions, findings, and conclusions or recommendations in this paper are those of the authors and do not necessarily reflect the views of NSF. ...
doi:10.1007/978-3-662-44202-9_10
fatcat:zyieeb2xlzce3e76zvmhevxpg4
Infrastructure-free logging and replay of concurrent execution on multiple cores
2014
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming - PPoPP '14
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During replay, our technique first tries to follow only the event order in the log. However, due to schedule differences, replay may fail. ...
The exploration is facilitated by a sophisticated caching mechanism. ...
Any opinions, findings, and conclusions or recommendations in this paper are those of the authors and do not necessarily reflect the views of NSF. ...
doi:10.1145/2555243.2555274
dblp:conf/ppopp/LeeKZ14
fatcat:4ydworc5q5hufeh5rfn6vf6lqi
Infrastructure-free logging and replay of concurrent execution on multiple cores
2014
SIGPLAN notices
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
During replay, our technique first tries to follow only the event order in the log. However, due to schedule differences, replay may fail. ...
The exploration is facilitated by a sophisticated caching mechanism. ...
Any opinions, findings, and conclusions or recommendations in this paper are those of the authors and do not necessarily reflect the views of NSF. ...
doi:10.1145/2692916.2555274
fatcat:6x2op4ukyrbbbpgqaan4v3ywx4
Parallelizing sequential network applications with customized lock-free data structures
2012
7th International Conference on Communications and Networking in China
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Single Program Multiple Data (SPMD) parallel programming style is used to retain the control flow of sequential code, and then data structures potentially involving data racing are replaced with customized ...
Parallelizing fine-grained network applications on general-purpose multi-core architectures is a big challenge, as it requires fast core-to-core synchronization that is not supported for now. ...
Although coarse-grained applications where a thread runs over a few milliseconds or seconds to finish a task can be successfully parallelized, fine-grained network applications in which a packet must be ...
doi:10.1109/chinacom.2012.6417441
dblp:conf/chinacom/WangZH12
fatcat:avukfn7bzjaifodz6xnxojbbou
A serializability violation detector for shared-memory server programs
2005
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation - PLDI '05
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We aim to improve reliability of multithreaded programs by proposing a dynamic detector that detects potentially erroneous program executions and their causes. ...
First, to detect only erroneous executions, SVD checks serializability of atomic regions, which are code regions that need to be executed atomically. ...
an earlier version of the paper. ...
doi:10.1145/1065010.1065013
dblp:conf/pldi/XuBH05
fatcat:q26l2vhqqndjjhhzgdrpt2iplm
A serializability violation detector for shared-memory server programs
2005
SIGPLAN notices
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We aim to improve reliability of multithreaded programs by proposing a dynamic detector that detects potentially erroneous program executions and their causes. ...
First, to detect only erroneous executions, SVD checks serializability of atomic regions, which are code regions that need to be executed atomically. ...
an earlier version of the paper. ...
doi:10.1145/1064978.1065013
fatcat:p6vx77bbj5bdzbngkn4ibiywwu
Instrumentation and sampling strategies for cooperative concurrency bug isolation
2010
SIGPLAN notices
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Static analyses to find crugs such as data races and atomicity violations scale poorly, while dynamic approaches incur high run-time overheads. ...
We also develop variant random sampling strategies that suit different types of predicates and help keep the run-time overhead low. ...
Many dynamic analysis tools have been proposed to detect data races and atomicity violation bugs [13, 14, 26, 36] . ...
doi:10.1145/1932682.1869481
fatcat:pf5qmf24bbc4tp3w4jyfgdg4ge
Instrumentation and sampling strategies for cooperative concurrency bug isolation
2010
Proceedings of the ACM international conference on Object oriented programming systems languages and applications - OOPSLA '10
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Static analyses to find crugs such as data races and atomicity violations scale poorly, while dynamic approaches incur high run-time overheads. ...
We also develop variant random sampling strategies that suit different types of predicates and help keep the run-time overhead low. ...
Many dynamic analysis tools have been proposed to detect data races and atomicity violation bugs [13, 14, 26, 36] . ...
doi:10.1145/1869459.1869481
dblp:conf/oopsla/JinTLL10
fatcat:k54jdispfbgobgadhu6fqow4om
Flexible Page-level Memory Access Monitoring Based on Virtualization Hardware
2017
Proceedings of the 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments - VEE '17
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In all, we propose a process-level virtual machine to achieve dynamic and fine-grained monitoring. Any application can run on our process-level virtual machine without modification. ...
We first introduce a memory allocator based on page protection that can achieve fine-grained monitoring. ...
Acknowledgments The authors will gratefully acknowledge the helpful suggestions of the reviewers, which have improved the presentation. ...
doi:10.1145/3050748.3050751
dblp:conf/vee/LuZWLN17
fatcat:wmayynyhpbgxrb3dkzwwsra5dq
Flexible Page-level Memory Access Monitoring Based on Virtualization Hardware
2017
SIGPLAN notices
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
In all, we propose a process-level virtual machine to achieve dynamic and fine-grained monitoring. Any application can run on our process-level virtual machine without modification. ...
We first introduce a memory allocator based on page protection that can achieve fine-grained monitoring. ...
Acknowledgments The authors will gratefully acknowledge the helpful suggestions of the reviewers, which have improved the presentation. ...
doi:10.1145/3140607.3050751
fatcat:e2qfvya3o5fjpmrsshjpph5mpe
Fine-Grained Acceleration of HMMER 3.0 via Architecture-Aware Optimization on Massively Parallel Processors
2015
2015 IEEE International Parallel and Distributed Processing Symposium Workshop
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At the fine-grained level, this technique naturally takes advantage of the concurrency of threads within a warp, and completely eliminates the overhead of synchronization. ...
In this work we target the core-segments of HMMER3 hmmsearch tool viz. the MSV and the P7Viterbi and present a fine grained parallelization scheme designed and implemented on Graphics Processing Units ...
ACKNOWLEDGMENT The authors would like to thank the Xilinx University Program(XUP) and the NVIDIA-Professor partnership for their generous support and donation helpful in carrying out this research. ...
doi:10.1109/ipdpsw.2015.107
dblp:conf/ipps/JiangG15
fatcat:jrfkmcob3jashn6un24rnnu5me
Efficient memory safety for TinyOS
2007
Proceedings of the 5th international conference on Embedded networked sensor systems - SenSys '07
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Our work improves this situation by providing efficient memory and type safety for TinyOS 2 applications running on the Mica2, MicaZ, and TelosB platforms. ...
Reliable sensor network software is difficult to create: applications are concurrent and distributed, hardware-based memory protection is unavailable, and severe resource constraints necessitate the use ...
This material is based upon work supported by the National Science Foundation under Grant Nos. 0448047, 0410285, and 0615367. ...
doi:10.1145/1322263.1322283
dblp:conf/sensys/CoopriderAEGR07
fatcat:55mti2ypgnhenl2yumgwjyrkna
Challenges in Defining a Programming Language for Provably Correct Dynamic Analyses
[chapter]
2012
Lecture Notes in Computer Science
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In this work we outline the challenges involved in developing Codana, a novel programming language for defining provably correct dynamic analyses. ...
A further challenge is effective but provably correct sharing: dynamic analyses can benefit from sharing information among another. ...
This work was supported by the German Federal Ministry of Education and Research (BMBF) within EC SPRIDE and by the Hessian LOEWE excellence initiative within CASED. ...
doi:10.1007/978-3-642-34026-0_2
fatcat:nwu6moz6hnaatguzdzf6k3vp2a
Barrier elision for production parallel programs
2015
SIGPLAN notices
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In our technique, we perform data race detection alongside the program to identify redundant barriers in their calling contexts; after an initial learning, we start eliding all future instances of barriers ...
In this paper, we present context-sensitive dynamic optimizations that elide barriers redundant during the program execution. ...
Acknowledgments Support for this work was provided in part through the X-Stack program funded by the U.S. ...
doi:10.1145/2858788.2688502
fatcat:ijdxemwqcvfefihilzvif3hyn4
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