Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches

Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance. Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application. Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time.Comment: Computers in Biology and Medicin

Towards Ad Hoc Recovery for Soft Errors

The coming exascale era is a great opportunity for high performance computing (HPC) applications. However, high failure rates on these systems will hazard the successful completion of their execution. Bit-flip errors in dynamic random access memory (DRAM) account for a noticeable share of the failures in supercomputers. Hardware mechanisms, such as error correcting code (ECC), can detect and correct single-bit errors and can detect some multi-bit errors while others can go undiscovered. Unfortunately, detected multi-bit errors will most of the time force the termination of the application and lead to a global restart. Thus, other strategies at the software level are needed to tolerate these type of faults more efficiently and to avoid a global restart. In this work, we extend the FTI checkpointing library to facilitate the implementation of custom recovery strategies for MPI applications, minimizing the overhead introduced when coping with soft errors. The new functionalities are evaluated by implementing local forward recovery on three HPC benchmarks with different reliability requirements. Our results demonstrate a reduction on the recovery times by up to 14%.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 708566 (DURO). This research is also supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the EU (Projects TIN2016-75845-P and the predoctoral grant of Nuria Losada ref. BES-2014-068066), and by the Galician Government (Xunta de Galicia) under the Consolidation Program of Competitive Research (ref. ED431C 2017/04).Peer ReviewedPostprint (author's final draft

Core Failure Mitigation in Integer Sum-of-Product Computations on Cloud Computing Systems

The decreasing mean-time-to-failure estimates in cloud computing systems indicate that multimedia applications running on such environments should be able to mitigate an increasing number of core failures at runtime. We propose a new roll-forward failure-mitigation approach for integer sumof-product computations, with emphasis on generic matrix multiplication (GEMM)and convolution/crosscorrelation (CONV) routines. Our approach is based on the production of redundant results within the numerical representation of the outputs via the use of numerical packing.This differs fromall existing roll-forward solutions that require a separate set of checksum (or duplicate) results. Our proposal imposes 37.5% reduction in the maximum output bitwidth supported in comparison to integer sum-ofproduct realizations performed on 32-bit integer representations which is comparable to the bitwidth requirement of checksummethods for multiple core failure mitigation. Experiments with state-of-the-art GEMM and CONV routines running on a c4.8xlarge compute-optimized instance of amazon web services elastic compute cloud (AWS EC2) demonstrate that the proposed approach is able to mitigate up to one quadcore failure while achieving processing throughput that is: 1) comparable to that of the conventional, failure-intolerant, integer GEMM and CONV routines, 2) substantially superior to that of the equivalent roll-forward failure-mitigation method based on checksum streams. Furthermore, when used within an image retrieval framework deployed over a cluster of AWS EC2 spot (i.e., low-cost albeit terminatable) instances, our proposal leads to: 1) 16%-23% cost reduction against the equivalent checksum-based method and 2) more than 70% cost reduction against conventional failure-intolerant processing on AWS EC2 on-demand (i.e., highercost albeit guaranteed) instances

Generalized Numerical Entanglement For Reliable Linear, Sesquilinear And Bijective Operations On Integer Data Streams

We propose a new technique for the mitigation of fail-stop failures and/or silent data corruptions (SDCs) within linear, sesquilinear or bijective (LSB) operations on M integer data streams (M ⩾ 3). In the proposed approach, the M input streams are linearly superimposed to form M numerically entangled integer data streams that are stored in-place of the original inputs, i.e., no additional (aka. “checksum”) streams are used. An arbitrary number of LSB operations can then be performed in M processing cores using these entangled data streams. The output results can be extracted from any (M-K) entangled output streams by additions and arithmetic shifts, thereby mitigating K fail-stop failures (K ≤ ⌊(M-1)/2 ⌋ ), or detecting up to K SDCs per M-tuple of outputs at corresponding in-stream locations. Therefore, unlike other methods, the number of operations required for the entanglement, extraction and recovery of the results is linearly related to the number of the inputs and does not depend on the complexity of the performed LSB operations. Our proposal is validated within an Amazon EC2 instance (Haswell architecture with AVX2 support) via integer matrix product operations. Our analysis and experiments for failstop failure mitigation and SDC detection reveal that the proposed approach incurs 0.75% to 37.23% reduction in processing throughput in comparison to the equivalent errorintolerant processing. This overhead is found to be up to two orders of magnitude smaller than that of the equivalent checksum-based method, with increased gains offered as the complexity of the performed LSB operations is increasing. Therefore, our proposal can be used in distributed systems, unreliable multicore clusters and safety-critical applications, where robustness against failures and SDCs is a necessity