Keeping checkpoint/restart viable for exascale systems

Next-generation exascale systems, those capable of performing a quintillion operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoints) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms

Lazy Fault Recovery for Redundant Mpi

Distributed Systems (DS) where multiple computers share a workload across a network, are used everywhere, from data intensive computations to storage and machine learning. DS provide a relatively cheap and efficient solution that allows stability with improved performance for computational intensive applications. In a DS faults and failures are the norm not the exception. At any moment data corruption can occur especially since a DS usually consists of hundred to thousands of units of commodity hardware. The large number and quality of components guarantees, by probability, that at any given time some of the components will not be working and some of them will not recover from failure. DS can experience problems caused by application bugs, operating systems bugs, failures with disks, memory, connectors, networking, power supply, and other components; therefore, constant monitoring and failure detection are fundamental. Automatic recovery must be integral to the system. One of the most commonly used programming languages for DS is Message Passing Interface (MPI). Unfortunately MPI does not support fault detection or recovery. In this thesis, we build a recovery mechanism based on replicas that works on top of the asynchronous fault detection implemented in previous work. Results shows that our recovery implementation is successful and the overhead in execution time is minimal

Resiliency in numerical algorithm design for extreme scale simulations

This work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 1023 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.Peer Reviewed"Article signat per 36 autors/es: Emmanuel Agullo, Mirco Altenbernd, Hartwig Anzt, Leonardo Bautista-Gomez, Tommaso Benacchio, Luca Bonaventura, Hans-Joachim Bungartz, Sanjay Chatterjee, Florina M. Ciorba, Nathan DeBardeleben, Daniel Drzisga, Sebastian Eibl, Christian Engelmann, Wilfried N. Gansterer, Luc Giraud, Dominik G ̈oddeke, Marco Heisig, Fabienne Jezequel, Nils Kohl, Xiaoye Sherry Li, Romain Lion, Miriam Mehl, Paul Mycek, Michael Obersteiner, Enrique S. Quintana-Ortiz, Francesco Rizzi, Ulrich Rude, Martin Schulz, Fred Fung, Robert Speck, Linda Stals, Keita Teranishi, Samuel Thibault, Dominik Thonnes, Andreas Wagner and Barbara Wohlmuth"Postprint (author's final draft

Resilience for Asynchronous Iterative Methods for Sparse Linear Systems

Large scale simulations are used in a variety of application areas in science and engineering to help forward the progress of innovation. Many spend the vast majority of their computational time attempting to solve large systems of linear equations; typically arising from discretizations of partial differential equations that are used to mathematically model various phenomena. The algorithms used to solve these problems are typically iterative in nature, and making efficient use of computational time on High Performance Computing (HPC) clusters involves constantly improving these iterative algorithms. Future HPC platforms are expected to encounter three main problem areas: scalability of code, reliability of hardware, and energy efficiency of the platform. The HPC resources that are expected to run the large programs are planned to consist of billions of processing units that come from more traditional multicore processors as well as a variety of different hardware accelerators. This growth in parallelism leads to the presence of all three problems. Previously, work on algorithm development has focused primarily on creating fault tolerance mechanisms for traditional iterative solvers. Recent work has begun to revisit using asynchronous methods for solving large scale applications, and this dissertation presents research into fault tolerance for fine-grained methods that are asynchronous in nature. Classical convergence results for asynchronous methods are revisited and modified to account for the possible occurrence of a fault, and a variety of techniques for recovery from the effects of a fault are proposed. Examples of how these techniques can be used are shown for various algorithms, including an analysis of a fine-grained algorithm for computing incomplete factorizations. Lastly, numerous modeling and simulation tools for the further construction of iterative algorithms for HPC applications are developed, including numerical models for simulating faults and a simulation framework that can be used to extrapolate the performance of algorithms towards future HPC systems

ParaMedic: Heterogeneous Parallel Error Correction

Processor error detection can be reduced in cost significantly by exploiting the parallelism that exists in a repeated copy of an execution, which may not exist in the original code, to split up the redundant work on a large number of small, highly efficient cores. However, such schemes don't provide a method for automatic error recovery. We develop ParaMedic, an architecture to allow efficient automatic correction of errors detected in a system by using parallel heterogeneous cores, to provide a full fail-safe system that does not propagate errors to other systems, and can recover without manual intervention. This uses logging to roll back any computation that occurred after a detected error, along with a set of techniques to provide error-checking parallelism while still preventing the escape of incorrect processor values in multicore environments, where ordering of individual processors' logs is not enough to be able to roll back execution. Across a set of single and multi-threaded benchmarks, we achieve 3.1\% and 1.5\% overhead respectively, compared with 1.9\% and 1\% for error detection alone.Arm Lt

Self-adaptivity of applications on network on chip multiprocessors: the case of fault-tolerant Kahn process networks

Technology scaling accompanied with higher operating frequencies and the ability to integrate more functionality in the same chip has been the driving force behind delivering higher performance computing systems at lower costs. Embedded computing systems, which have been riding the same wave of success, have evolved into complex architectures encompassing a high number of cores interconnected by an on-chip network (usually identified as Multiprocessor System-on-Chip). However these trends are hindered by issues that arise as technology scaling continues towards deep submicron scales. Firstly, growing complexity of these systems and the variability introduced by process technologies make it ever harder to perform a thorough optimization of the system at design time. Secondly, designers are faced with a reliability wall that emerges as age-related degradation reduces the lifetime of transistors, and as the probability of defects escaping post-manufacturing testing is increased. In this thesis, we take on these challenges within the context of streaming applications running in network-on-chip based parallel (not necessarily homogeneous) systems-on-chip that adopt the no-remote memory access model. In particular, this thesis tackles two main problems: (1) fault-aware online task remapping, (2) application-level self-adaptation for quality management. For the former, by viewing fault tolerance as a self-adaptation aspect, we adopt a cross-layer approach that aims at graceful performance degradation by addressing permanent faults in processing elements mostly at system-level, in particular by exploiting redundancy available in multi-core platforms. We propose an optimal solution based on an integer linear programming formulation (suitable for design time adoption) as well as heuristic-based solutions to be used at run-time. We assess the impact of our approach on the lifetime reliability. We propose two recovery schemes based on a checkpoint-and-rollback and a rollforward technique. For the latter, we propose two variants of a monitor-controller- adapter loop that adapts application-level parameters to meet performance goals. We demonstrate not only that fault tolerance and self-adaptivity can be achieved in embedded platforms, but also that it can be done without incurring large overheads. In addressing these problems, we present techniques which have been realized (depending on their characteristics) in the form of a design tool, a run-time library or a hardware core to be added to the basic architecture

Checkpoint-based forward recovery using lookahead execution and rollback validation in parallel and distributed systems

This thesis studies a forward recovery strategy using checkpointing and optimistic execution in parallel and distributed systems. The approach uses replicated tasks executing on different processors for forwared recovery and checkpoint comparison for error detection. To reduce overall redundancy, this approach employs a lower static redundancy in the common error-free situation to detect error than the standard N Module Redundancy scheme (NMR) does to mask off errors. For the rare occurrence of an error, this approach uses some extra redundancy for recovery. To reduce the run-time recovery overhead, look-ahead processes are used to advance computation speculatively and a rollback process is used to produce a diagnosis for correct look-ahead processes without rollback of the whole system. Both analytical and experimental evaluation have shown that this strategy can provide a nearly error-free execution time even under faults with a lower average redundancy than NMR

GPU 에러 안정성 보장을 위한 컴파일러 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2020. 8. 이재진.Due to semiconductor technology scaling and near-threshold voltage computing, soft error resilience has become more important. Nowadays, GPUs are widely used in high performance computing (HPC) because of its efficient parallel processing and modern GPUs designed for HPC use error correction code (ECC) to protect their storage including register files. However, adopting ECC in the register file imposes high area and energy overhead. To replace the expensive hardware cost of ECC, we propose Penny, a lightweight compiler-directed resilience scheme for GPU register file protection. We combine recent advances in idempotent recovery with low-cost error detection code. Our approach focuses on solving two important problems: 1. Can we guarantee correct error recovery using idempotent execution with error detection code? We show that when an error detection code is used with idempotence recovery, certain restrictions required by previous idempotent recovery schemes are no longer needed. We also propose a software-based scheme to prevent the checkpoint value from being overwritten before the end of the region where the value is required for correct recovery. 2. How do we reduce the execution overhead caused by checkpointing? In GPUs additional checkpointing store instructions inflicts considerably higher overhead compared to CPUs, due to its architectural characteristics, such as lack of store buffers. We propose a number of compiler optimizations techniques that significantly reduce the overhead.반도체 미세공정 기술이 발전하고 문턱전압 근처 컴퓨팅(near-threashold voltage computing)이 도입됨에 따라서 소프트 에러로부터의 복원이 중요한 과제가 되었다. 강력한 병렬 계산 성능을 지닌 GPU는 고성능 컴퓨팅에서 중요한 위치를 차지하게 되었고, 슈퍼 컴퓨터에서 쓰이는 GPU들은 에러 복원 코드인 ECC를 사용하여 레지스터 파일 및 메모리 등에 저장된 데이터를 보호하게 되었다. 하지만 레지스터 파일에 ECC를 사용하는 것은 큰 하드웨어나 에너지 비용을 필요로 한다. 이런 값비싼 ECC의 하드웨어 비용을 줄이기 위해 본 논문에서는 컴파일러 기반의 저비용 GPU 레지스터 파일 복원 기법인 Penny를 제안한다. 이는 최신의 멱등성(idempotency) 기반 에러 복원 기법을 저비용의 에러 검출 코드(EDC)와 결합한 것이다. 본 논문은 다음 두가지 문제를 해결하는 데에 집중한다. 1. 에러 검출 코드 기반으로 멱등성 기반 에러 복원을 사용시 소프트 에러로부터의 안전한 복원을 보장할 수 있는가?} 본 논문에서는 에러 검출 코드가 멱등성 기반 복원 기술과 같이 사용되었을 경우 기존의 복원 기법에서 필요로 했던 조건들 없이도 안전하게 에러로부터 복원할 수 있음을 보인다. 2. 체크포인팅에드는 비용을 어떻게 절감할 수 있는가?} GPU는 스토어 버퍼가 없는 등 아키텍쳐적인 특성으로 인해서 CPU와 비교하여 체크포인트 값을 저장하는 데에 큰 오버헤드가 든다. 이 문제를 해결하기 위해 본 논문에서는 다양한 컴파일러 최적화 기법을 통하여 오버헤드를 줄인다.1 Introduction 1 1.1 Why is Soft Error Resilience Important in GPUs 1 1.2 How can the ECC Overhead be Reduced 3 1.3 What are the Challenges 4 1.4 How do We Solve the Challenges 5 2 Comparison of Error Detection and Correction Coding Schemes for Register File Protection 7 2.1 Error Correction Codes and Error Detection Codes 8 2.2 Cost of Coding Schemes 9 2.3 Soft Error Frequency of GPUs 11 3 Idempotent Recovery and Challenges 13 3.1 Idempotent Execution 13 3.2 Previous Idempotent Schemes 13 3.2.1 De Kruijf's Idempotent Translation 14 3.2.2 Bolts's Idempotent Recovery 15 3.2.3 Comparison between Idempotent Schemes 15 3.3 Idempotent Recovery Process 17 3.4 Idempotent Recovery Challenges for GPUs 18 3.4.1 Checkpoint Overwriting 20 3.4.2 Performance Overhead 20 4 Correctness of Recovery 22 4.1 Proof of Safe Recovery 23 4.1.1 Prevention of Error Propagation 23 4.1.2 Proof of Correct State Recovery 24 4.1.3 Correctness in Multi-Threaded Execution 28 4.2 Preventing Checkpoint Overwriting 30 4.2.1 Register renaming 31 4.2.2 Storage Alternation by Checkpoint Coloring 33 4.2.3 Automatic Algorithm Selection 38 4.2.4 Future Works 38 5 Performance Optimizations 40 5.1 Compilation Phases of Penny 40 5.1.1 Region Formation 41 5.1.2 Bimodal Checkpoint Placement 41 5.1.3 Storage Alternation 42 5.1.4 Checkpoint Pruning 43 5.1.5 Storage Assignment 44 5.1.6 Code Generation and Low-level Optimizations 45 5.2 Cost Estimation Model 45 5.3 Region Formation 46 5.3.1 De Kruijf's Heuristic Region Formation 46 5.3.2 Region splitting and Region Stitching 47 5.3.3 Checkpoint-Cost Aware Optimal Region Formation 48 5.4 Bimodal Checkpoint Placement 52 5.5 Optimal Checkpoint Pruning 55 5.5.1 Bolt's Naive Pruning Algorithm and Overview of Penny's Optimal Pruning Algorithm 55 5.5.2 Phase 1: Collecting Global-Decision Independent Status 56 5.5.3 Phase2: Ordering and Finalizing Renaming Decisions 60 5.5.4 Effectiveness of Eliminating the Checkpoints 63 5.6 Automatic Checkpoint Storage Assignment 69 5.7 Low-Level Optimizations and Code Generation 70 6 Evaluation 74 6.1 Test Environment 74 6.1.1 GPU Architecture and Simulation Setup 74 6.1.2 Tested Applications 75 6.1.3 Register Assignment 76 6.2 Performance Evaluation 77 6.2.1 Overall Performance Overheads 77 6.2.2 Impact of Penny's Optimizations 78 6.2.3 Assigning Checkpoint Storage and Its Integrity 79 6.2.4 Impact of Optimal Checkpoint Pruning 80 6.2.5 Impact of Alias Analysis 81 6.3 Repurposing the Saved ECC Area 82 6.4 Energy Impact on Execution 83 6.5 Performance Overhead on Volta Architecture 85 6.6 Compilation Time 85 7 RelatedWorks 87 8 Conclusion and Future Works 89 8.1 Limitation and Future Work 90Docto

Scalable Energy-efficient Microarchitectures with Computational Error Tolerance

Dennard scaling of conventional semiconductor technology has reached its limit resulting in issues pertaining to leakage current and threshold voltage. Energy-savings found at the transistor level by simply lowering supply voltage are no longer available for these devices (e.g., MOSFETs) and has reached the Landauer-Shannon limit. Recent proposals of minivolt switch technologies aim to extend the technology scaling roadmap by maintaining a high on/off ratio of drain current with a much lower supply voltage. However, high intermittent error probabilities in millivolt switches constraints their Vdd reduction for traditional architectures. Thus, there is an urgent need for scalable and energy-efficient micro-architectures with computational error-tolerance. This thesis systematically leverages the error detection and correction properties of the Redundant Residue Number System (RRNS) by varying the number of non-redundant (n) and redundant (r) components (residues), and selects and discusses trade-offs about configuration points from a two-dimensional (n, r)-RRNS design plane that meet certain capabilities of error detection and/or correction. Being able to efficiently handle resilience in this (n, r)-RRNS plane significantly improves reliability, allowing further Vdd reduction and energy savings. First, the necessary implementation details of RRNS cores are discussed. Second, scalable RRNS micro-architectures that simultaneously support both error-correction and checkpointing with restart capabilities for uncorrectable errors are proposed. Third, novel RRNS-based adaptive checkpointing&restart mechanisms are designed that automatically guarantee reliability while minimizing the energy-delay product (EDP). Finally, the RRNS design space is explored to find the optimal (n, r) configuration points. For similar reliability when compared to a conventional binary core (running at high Vdd) without computational error tolerance, the proposed RRNS scalable micro-architecture reduces EDP by 53% on average for memory-intensive workloads and by 67% on average for non-memory-intensive workloads. This thesis's second topic is to alleviate fault rate and power consumption issues of exascale computing. Faults in High-Performance Computing (HPC) have become an urgent challenge with estimated Mean Time Between Failures (MTBF) of exascale system projected as only several minutes with contemporary methodologies. Unfortunately, existing error-tolerance technologies in the context of HPC systems have serious deficiencies such as insufficient error-tolerance coverage, high power consumption, and difficult integration with existing workloads. Considering Department of Energy (DOE) guidelines that limit exascale power consumption to 20 MW, this thesis highlights the issue of energy usage and proposes a thread-level fault tolerance mechanism compatible with current state-of-the art exascale programming models while simultaneously meeting the requirements of full system error protection. Additionally, an efficient micro-architecture and corresponding mechanisms that can support thread level RRNS are discussed. Experimental results show that this strategy reduces energy consumption by 62.25% and the Energy-Delay-Product by 58.67% on average when compared with state-of-the-art black box resilience techniques.Ph.D