OPR

The ability to reproduce a parallel execution is desirable for debugging and program reliability purposes. In debugging (13), the programmer needs to manually step back in time, while for resilience (6) this is automatically performed by the the application upon failure. To be useful, replay has to faithfully reproduce the original execution. For parallel programs the main challenge is inferring and maintaining the order of conflicting operations (data races). Deterministic record and replay (R&R) techniques have been developed for multithreaded shared memory programs (5), as well as distributed memory programs (14). Our main interest is techniques for large scale scientific (3; 4) programming models

A Survey of Fault-Tolerance and Fault-Recovery Techniques in Parallel Systems

Supercomputing systems today often come in the form of large numbers of commodity systems linked together into a computing cluster. These systems, like any distributed system, can have large numbers of independent hardware components cooperating or collaborating on a computation. Unfortunately, any of this vast number of components can fail at any time, resulting in potentially erroneous output. In order to improve the robustness of supercomputing applications in the presence of failures, many techniques have been developed to provide resilience to these kinds of system faults. This survey provides an overview of these various fault-tolerance techniques.Comment: 11 page

Execution replay and debugging

As most parallel and distributed programs are internally non-deterministic -- consecutive runs with the same input might result in a different program flow -- vanilla cyclic debugging techniques as such are useless. In order to use cyclic debugging tools, we need a tool that records information about an execution so that it can be replayed for debugging. Because recording information interferes with the execution, we must limit the amount of information and keep the processing of the information fast. This paper contains a survey of existing execution replay techniques and tools.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop on Automated Debugging (AADebug 2000), August 2000, Munich. cs.SE/001003

A fault-tolerance protocol for parallel applications with communication imbalance

ArticuloThe predicted failure rates of future supercomputers loom the groundbreaking research large machines are expected to foster. Therefore, resilient extreme-scale applications are an absolute necessity to effectively use the new generation of supercomputers. Rollback-recovery techniques have been traditionally used in HPC to provide resilience. Among those techniques, message logging provides the appealing features of saving energy, accelerating recovery, and having low performance penalty. Its increased memory consumption is, however, an important downside. This paper introduces memory-constrained message logging (MCML), a general framework for decreasing the memory footprint of message-logging protocols. In particular, we demonstrate the effectiveness of MCML in maintaining message logging feasible for applications with substantial communication imbalance. This type of applications appear in many scientific fields. We present experimental results with several parallel codes running on up to 4,096 cores. Using those results and an analytical model, we predict MCML can reduce execution time up to 25% and energy consumption up to 15%, at extreme scale

Impact of Event Logger on Causal Message Logging Protocols for Fault Tolerant {MPI}

International audienceFault tolerance in MPI becomes a main issue in the HPC community. Several approaches are envisioned from user or programmer controlled fault tolerance to fully automatic fault detection and handling. For this last approach, several protocols have been proposed in the literature. In a recent paper, we have demonstrated that uncoordinated checkpointing tolerates higher fault frequency than coordinated checkpointing. Moreover causal message logging protocols have been proved the most efficient message logging technique. These protocols consist in piggybacking non deterministic events to computation message. Several protocols have been proposed in the literature. Their merits are usually evaluated from four metrics: a) piggybacking computation cost, b) piggyback size, c) applications performance and d) fault recovery performance. In this paper, we investigate the benefit of using a stable storage for logging message events in causal message logging protocols. To evaluate the advantage of this technique we implemented three protocols: 1) a classical causal message protocol proposed in Manetho, 2) a state of the art protocol known as LogOn, 3) a light computation cost protocol called Vcausal. We demonstrate a major impact of this stable storage for the three protocols, on the four criteria for micro benchmarks as well as for the NAS benchmark

Detailed Diagnosis of Performance Anomalies in Sensornets

We address the problem of analysing performance anomalies in sensor networks. In this paper, we propose an approach that uses the local flash storage of the motes for logging system data, in combination with online statistical analysis. Our results show not only that this is a feasible method but that the overhead is significantly lower than that of communication-centric methods, and that interesting patterns can be revealed when calculating the correlation of large data sets of separate event types.GINSENGCONE

Lightweight Asynchronous Snapshots for Distributed Dataflows

Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput. One of the most fundamental challenges of this paradigm is providing processing guarantees under potential failures. Existing approaches rely on periodic global state snapshots that can be used for failure recovery. Those approaches suffer from two main drawbacks. First, they often stall the overall computation which impacts ingestion. Second, they eagerly persist all records in transit along with the operation states which results in larger snapshots than required. In this work we propose Asynchronous Barrier Snapshotting (ABS), a lightweight algorithm suited for modern dataflow execution engines that minimises space requirements. ABS persists only operator states on acyclic execution topologies while keeping a minimal record log on cyclic dataflows. We implemented ABS on Apache Flink, a distributed analytics engine that supports stateful stream processing. Our evaluation shows that our algorithm does not have a heavy impact on the execution, maintaining linear scalability and performing well with frequent snapshots.Comment: 8 pages, 7 figure