Extending a traditional debugger to debug massively parallel applications

Balle, Susanne M.; Brett, Bevin; Chen, Chih‐Ping; LaFrance-Linden, David

doi:10.1016/j.jpdc.2004.03.012

Cited by 20 publications

(12 citation statements)

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“…Realizing the importance of the reliability of parallel and distributed programs, researchers have proposed many dynamic techniques for interactive parallel debugging [40], [41], [42], [43], [44], [45], [46] and automatic bug detection [12], [19], [24], [47], [48], [49], [50]. Interactive parallel debuggers help programmers identify the bugs by exploiting automated information collection, aggregation, and visualization techniques.…”

Section: Bug Detection For Parallel and Distributed Programsmentioning

confidence: 99%

SyncChecker: Detecting Synchronization Errors between MPI Applications and Libraries

Chen

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium

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We have implemented a prototype of SyncChecker on Linux and evaluated it with seven bug cases, i.e., five introduced by the original developers and two injected, in four different MPI applications. Our experiments show that SyncChecker detects all the evaluated synchronization errors and provides helpful diagnostic information. Moreover, our experiments with seven NAS Parallel Benchmarks demonstrate that SyncChecker incurs moderate runtime overhead, 1.3-9.5 times with an average of 5.2 times, making it suitable for software testing.

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Section: Bug Detection For Parallel and Distributed Programsmentioning

confidence: 99%

SyncChecker: Detecting Synchronization Errors between MPI Applications and Libraries

Chen

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium

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“…In addition to these dynamic approaches, researchers have explored formal verification and model checking methods [49], [55] for detecting bugs such as deadlocks in parallel and distributed programs. Moreover, interactive parallel debuggers [9], [11], [15], [24], [37] leverage automated information collection, aggregation, and visualization to help programmers manually locate root causes of software bugs in parallel programs. In summary, these studies focus on detecting software bugs at the application level.…”

Section: Related Workmentioning

confidence: 99%

FlowChecker: Detecting Bugs in MPI Libraries via Message Flow Checking

Chen

Gao

Zhang

et al. 2010

2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Many MPI libraries have suffered from software bugs, which severely impact the productivity of a large number of users. This paper presents a new method called FlowChecker for detecting communication-related bugs in MPI libraries. The main idea is to extract program intentions of message passing (MPintentions), and to check whether these MP-intentions are fulfilled correctly by the underlying MPI libraries, i.e., whether messages are delivered correctly from specified sources to specified destinations. If not, FlowChecker reports the bugs and provides diagnostic information.We have built a FlowChecker prototype on Linux and evaluated it with five real-world bug cases in three widely-used MPI libraries, including Open MPI, MPICH2, and MVAPICH2. Our experimental results show that FlowChecker effectively detects all five evaluated bug cases and provides useful diagnostic information. Additionally, our experiments with HPL and NPB show that FlowChecker incurs low runtime overhead (0.9-9.7% on three MPI libraries).

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“…While we present this using STAT results, it captures general problems for contemporary scalable tool research, in particular with trends towards scalably combining reusable, serial components using a communication infrastructure [4], [7], [11].…”

Section: File System Issues and Access To Static Informationmentioning

confidence: 99%

“…Full-featured debuggers such as TotalView [14] and DDT [11] have been run on thousands of processes, but typically suffer high latencies for even simple operations at these scales. The Ladebug project [4] aims at providing scalable parallel debugging and presented a prototype debugger on top of a hierarchical communication network similar to MRNet used in STAT. However, Ladebug has not been evaluated at the scales targeted in this work.…”

Section: Related Workmentioning

confidence: 99%

Lessons learned at 208K: Towards debugging millions of cores

L¹,

Ahn²,

Arnold³

et al. 2008

2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Petascale systems will present several new challenges to performance and correctness tools. Such machines may contain millions of cores, requiring that tools use scalable data structures and analysis algorithms to collect and to process application data. In addition, at such scales, each tool itself will become a large parallel application -already, debugging the full BlueGene/L (BG/L) installation at the Lawrence Livermore National Laboratory requires employing 1664 tool daemons. To reach such sizes and beyond, tools must use a scalable communication infrastructure and manage their own tool processes efficiently. Some system resources, such as the file system, may also become tool bottlenecks.In this paper, we present challenges to petascale tool development, using the Stack Trace Analysis Tool (STAT) as a case study. STAT is a lightweight tool that gathers and merges stack traces from a parallel application to identify process equivalence classes. We use results gathered at thousands of tasks on an Infiniband cluster and results up to 208K processes on BG/L to identify current scalability issues as well as challenges that will be faced at the petascale. We then present implemented solutions to these challenges and show the resulting performance improvements. We also discuss future plans to meet the debugging demands of petascale machines.

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Extending a traditional debugger to debug massively parallel applications

Cited by 20 publications

References 1 publication

SyncChecker: Detecting Synchronization Errors between MPI Applications and Libraries

SyncChecker: Detecting Synchronization Errors between MPI Applications and Libraries

FlowChecker: Detecting Bugs in MPI Libraries via Message Flow Checking

Lessons learned at 208K: Towards debugging millions of cores

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