Checkpointing vs. Migration for Post-Petascale Machines

We craft a few scenarios for the execution of sequential and parallel jobs on future generation machines. Checkpointing or migration, which technique to choose

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Checkpointing vs. Migration for Post-Petascale Machines

We craft a few scenarios for the execution of sequential and parallel jobs on future generation machines. Checkpointing or migration, which technique to choose

Resilience Issues for Application Workflows on Clouds

International audienceTwo areas are currently the focus of active research, namely cloud computing and high-performance computing. Their expected impact on business and scientific computing is such that most application areas are eagerly uptaking or waiting for the associated infrastructures. However, open issues still remain. Resilience and loadbalancing are examples of such areas where innovative solutions are required to face new or increasing challenges, e.g., fault-tolerance. This paper presents existing concepts and open issues related to the design, implementation and deployment of a fault-tolerant application framework on cloud computing platforms. Experiments are sketched including the support for application resilience, i.e., faulttolerance and exception-handling. They also support the transparent execution of distributed codes on remote highperformance clusters

Algorithm Based Fault Tolerance: A Perspective from Algorithmic and Communication Characteristics of Parallel Algorithms

Checkpoint and recovery cost imposed by checkpoint/restart (CP/R) is a crucial performance issue for high-performance computing (HPC) applications. In comparison, Algorithm-Based Fault Tolerance (ABFT) is a promising fault tolerance method with low recovery overhead, but it suffers from the inadequacy of universal applicability, i.e., tied to a specific application or algorithm. Till date, providing fault tolerance for matrix-based algorithms for linear systems has been the research focus of ABFT schemes. As a consequence, it necessitates a comprehensive exploration of ABFT research to widen its scope to other types of parallel algorithms and applications. In this thesis, we go beyond traditional ABFT and focus on other types of parallel applications not covered by traditional ABFT. In that regard, rather than an emphasis on a single application at a time, we consider the algorithmic and communication characteristics of a class of parallel applications to design efficient fault tolerance and recovery strategies for that class of parallel applications. The communication characteristics determine how to distributively replicate the fault recovery data (we call it the {\em critical data}) of a process, and the algorithmic characteristics determine what the application-specific data is to be replicated to minimize fault tolerance and recovery cost. Based on communication characteristics, parallel algorithms can be broadly classified as (i) embarrassingly parallel algorithms, where processes have infrequent or rare interactions, and (ii) communication-intensive parallel algorithms, where processes have significant interactions. In this thesis, through different case studies, we design ABFT for these two categories of algorithms by considering their algorithmic and communication characteristics. Analysis of these parallel algorithms reveals that a process contains sufficient information that can help to rebuild a computational state if any failure occurs during the computation. We define this information as critical data, the minimal application-level data required to be saved (securely) so that a failed process can be fully recovered from a most recent consistent state using this fault recovery data. How the communication dependencies among processes are utilized to replicate fault recovery data is directly related to the system’s fault tolerance performance. We propose ABFT for parallel search algorithms, which belong to the class of embarrassingly parallel algorithms. Parallel search algorithms are the well-known solution techniques for discrete optimization problems (DOP). DOP covers a broad class of (parallel) applications from search problems in AI to computer games, e.g., Chess and various games, traveling salesman problem, various AI search problems. As a case study, we choose the parallel iterative deepening A* (PIDA*) algorithm and integrate application-level fault tolerance with the algorithm by replicating critical data periodically to make it resilient. In the category of communication-intensive algorithms, we choose Dynamic programming (DP) which is a widely used algorithm paradigm for optimization problems. We choose parallel DP algorithm as a case study and propose ABFT for such applications. We present a detailed analysis of the characteristics of parallel DP algorithms and show that the algorithmic features reduce the cardinality of critical data into a single data in case of $n$-data dependent task. We demonstrate the idea with two popular DP class of applications: (i) the traveling salesman problem (TSP), and (ii) the longest common subsequence (LCS) problem. Minimal storage and recovery overhead are the prime concern in FT design. On that regard, we demonstrate that further optimization in critical data is possible for particular DP class of problems, where the degree of dependency for a subproblem is small and fixed at each iteration. We discuss it with the 0/1 knapsack problem as a case study and propose an ABFT scheme where, instead of replicating the critical data, we replicate a bit-vector flag in peer process's memory which is later used to rebuild the lost data of a failed process. Theoretical and experimental results demonstrate that our proposed methods perform significantly better than the conventional CP/R in terms of fault tolerance and recovery overheads, and also in storage overhead in the presence of single and multiple simultaneous failures

Failure avoidance techniques for HPC systems based on failure prediction

A increasingly larger percentage of computing capacity in today's large high-performance computing systems is wasted due to failures and recoveries. Moreover, it is expected that high performance computing will reach exascale within a decade, decreasing the mean time between failures to one day or even a few hours, making fault tolerance a major challenge for the HPC community. As a consequence, current research is focusing on providing fault tolerance strategies that aim to minimize fault's effects on applications. By far, the most popular and used techniques from this field are rollback-recovery protocols. However, existing rollback-recovery techniques have severe scalability limitations and without further optimizations the use of current protocols is put under serious questions for future exascale systems. A way of reducing the overhead induced by these strategies is by combining them with failure avoidance methods. Failure avoidance is based on a prediction model that detects fault occurrences ahead of time and allows preventive measures to be taken, such as task migration or checkpointing the application before failure. The same methodology can be generalized and applied to anomaly avoidance, where anomaly can mean anything from system failures to performance degradation at the application level. For this, monitoring systems require a reliable prediction system to give information on when failures will occur and at what location. Thus far, research in this field used ideal predictors that do not have any implementation in real HPC systems. This thesis focuses on analyzing and characterizing anomaly patterns at both the application and system levels and on offering solutions to prevent anomalies from affecting applications running in the system. Currently, there is no good characterization of normal behavior for system state data or how different components react to failures within HPC systems. For example, in case a node experiences a network failure and is incapable of generating log messages, the failure is announced in the log files by a lack of generated messages. Conversely, some component failures may cause logging a large numbers of notifications. For example, memory failures can result in a single faulty component generating hundreds or thousands of messages in less than a day. It is important to be able to capture the behavior of each event type and understand what is the normal behavior and how each failure type affects it. This idea represents the building block of a novel way of characterizing the state of the system in time by analyzing the properties of each event described in different system metrics, considering its own trend and behavior. The method introduces the integration between signal processing concepts and data mining techniques in the context of analysis for large-scale systems. By shaping the normal and faulty behavior of each event and of the whole system, appropriate models and methods for descriptive and forecasting purposes are proposed. After having an accurate overview of the whole system, the thesis analyzes how the prediction model impacts current fault tolerance techniques and in the end integrates it into a fault avoidance solution. This hybrid protocol optimizes the overhead that current fault tolerance strategies impose on applications and presents a viable solution for future large-scale systems

Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives

© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, Vol. 53, No. 5, Article 95. Publication date: September 2020. https://doi.org/10.1145/3403956[EN] Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.This work has received funding from the European Union's Horizon 2020 (H2020) research and innovation program under the FET-HPC Grant Agreement No. 801137 (RECIPE). Jaume Abella was also partially supported by the Ministry of Economy and Competitiveness of Spain under Contract No. TIN2015-65316-P and under Ramon y Cajal Postdoctoral Fellowship No. RYC-2013-14717, as well as by the HiPEAC Network of Excellence. Ramon Canal is partially supported by the Generalitat de Catalunya under Contract No. 2017SGR0962.Canal, R.; Hernández Luz, C.; Tornero-Gavilá, R.; Cilardo, A.; Massari, G.; Reghenzani, F.; Fornaciari, W.... (2020). Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives. ACM Computing Surveys. 53(5):1-32. https://doi.org/10.1145/3403956S132535Abella, J., Hernandez, C., Quinones, E., Cazorla, F. J., Conmy, P. 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IEEE Internet Com

BlobCR: Efficient Checkpoint-Restart for HPC Applications on IaaS Clouds using Virtual Disk Image Snapshots

International audienceInfrastructure-as-a-Service (IaaS) cloud computing is gaining significant interest in industry and academia as an alternative platform for running scientific applications. Given the dynamic nature of IaaS clouds and the long runtime and resource utilization of such applications, an efficient checkpoint-restart mechanism becomes paramount in this context. This paper proposes a solution to the aforementioned challenge that aims at minimizing the storage space performance overhead of checkpoint-restart. We introduce a framework that combines checkpoint-restart protocols at guest level with virtual machine (VM) disk-image multi-snapshotting and multi-deployment at host level in order to efficiently capture and potentially roll back the complete state of the application, including file system modifications. Experiments on the G5K testbed show substantial improvement for MPI applications over existing approaches, both for the case when customized checkpointing is available at application level and the case when it needs to be handled at process level

Monitoring, analysis and optimisation of I/O in parallel applications

High performance computing (HPC) is changing the way science is performed in the 21st Century; experiments that once took enormous amounts of time, were dangerous and often produced inaccurate results can now be performed and refined in a fraction of the time in a simulation environment. Current generation supercomputers are running in excess of 1016 floating point operations per second, and the push towards exascale will see this increase by two orders of magnitude. To achieve this level of performance it is thought that applications may have to scale to potentially billions of simultaneous threads, pushing hardware to its limits and severely impacting failure rates. To reduce the cost of these failures, many applications use checkpointing to periodically save their state to persistent storage, such that, in the event of a failure, computation can be restarted without significant data loss. As computational power has grown by approximately 2x every 18 ? 24 months, persistent storage has lagged behind; checkpointing is fast becoming a bottleneck to performance. Several software and hardware solutions have been presented to solve the current I/O problem being experienced in the HPC community and this thesis examines some of these. Specifically, this thesis presents a tool designed for analysing and optimising the I/O behaviour of scientific applications, as well as a tool designed to allow the rapid analysis of one software solution to the problem of parallel I/O, namely the parallel log-structured file system (PLFS). This thesis ends with an analysis of a modern Lustre file system under contention from multiple applications and multiple compute nodes running the same problem through PLFS. The results and analysis presented outline a framework through which application settings and procurement decisions can be made

What Makes Affinity-Based Schedulers So Efficient ?

The tremendous increase in the size and heterogeneity of supercomputers makes it very difficult to predict the performance of a scheduling algorithm. Therefore, dynamic solutions, where scheduling decisions are made at runtime have overpassed static allocation strategies. The simplicity and efficiency of dynamic schedulers such as Hadoop are a key of the success of the MapReduce framework. Dynamic schedulers such as StarPU, PaRSEC or StarSs are also developed for more constrained computations, e.g. task graphs coming from linear algebra. To make their decisions, these runtime systems make use of some static information, such as the distance of tasks to the critical path or the affinity between tasks and computing resources (CPU, GPU,\ldots) and of dynamic information, such as where input data are actually located. In this paper, we concentrate on two elementary linear algebra kernels, namely the outer product and the matrix multiplication. For each problem, we propose several dynamic strategies that can be used at runtime and we provide an analytic study of their theoretical performance. We prove that the theoretical analysis provides very good estimate of the amount of communications induced by a dynamic strategy, thus enabling to choose among them for a given problem and architecture