Concepts for OpenMP Target Offload Resilience

We design resource management heuristics that assign serial tasks to the nodes of a heterogeneous high performance computing (HPC) system. The value of completing these tasks is modeled using monotonically decreasing utility functions that represent the time-varying importance of the task. The value of completing a task is equal to its utility function at the time of its completion. The overall performance of this system is measured using the total utility earned by all tasks during some interval of time. To maximize the performance of such a system where the preemption of tasks is possible, we have designed, analyzed, and compared a set of resource allocation heuristic techniques. We combine two utility-aware heuristics with three different preemption techniques to create six preemption-capable heuristics. We also consider the two utility-aware heuristics without preemption. We use simulation studies to evaluate this set of eight heuristics and compare them with an FCFS heuristic, which is often used in real systems, and random assignments. In general, our set of eight heuristics is able to significantly outperform the comparison heuristics, and the preemption-capable heuristics are able to significantly increase the utility earned compared to the heuristics that do not use preemption. We analyze the performance tradeoffs among the different preemption-capable heuristics under a variety of oversubscribed workload environments.

The main goal of the ANTAREX project is to express by a Domain Specific Language (DSL) the application self-adaptivity and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to the Exascale level. Key innovations of the project include the introduction of a separation of concerns between self-adaptivity strategies and application functionalities. The DSL approach will allow the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management.

Ideally, a heterogeneous computing (HC) environment, is a well-orchestrated and coordinates suite of high-performance machines that provides support for computationally intensive applications with diverse computing requirements. Such an HC system includes a heterogeneous suite of machines, high-speed interconnections, interfaces, operating systems, communication protocols, and programming environments. HC is the effective use of these diverse hardware and software components to meet the distinct and varied computational requirements of a given application. Implicit in this concept of HC is the idea that subtasks with different machine architectural requirements are embedded in the applications executed by the HC system. Two types of HC systems, mixed-mode machines and mixed-machine systems, are discussed. The goals of and open problems in HC are overviewed.

This study considers a heterogeneous computing system and corresponding workload being investigated by the Extreme Scale Systems Center (ESSC) at Oak Ridge National Laboratory (ORNL). The ESSC is part of a collaborative effort between the Department of Energy (DOE) and the Department of Defense (DoD) to deliver research, tools, software, and technologies that can be integrated, deployed, and used in both DOE and DoD environments. The heterogeneous system and workload described here are representative of a prototypical computing environment being studied as part of this collaboration. Each task can exhibit a time-varying emph{importance} or emph{utility} to the overall enterprise. In this system, an arriving task has an associated priority and precedence. The priority is used to describe the importance of a task, and precedence is used to describe how soon the task must be executed. These two metrics are combined to create a utility function curve that indicates how valuable it is for the system to complete a task at any given moment. This research focuses on using time-utility functions to generate a metric that can be used to compare the performance of different resource schedulers in a heterogeneous computing system. The contributions of this paper are: (a) a mathematical model of a heterogeneous computing system where tasks arrive dynamically and need to be assigned based on their priority, precedence, utility characteristic class, and task execution type, (b) the use of priority and precedence to generate time-utility functions that describe the value a task has at any given time, (c) the derivation of a metric based on the total utility gained from completing tasks to measure the performance of the computing environment, and (d) a comparison of the performance of resource allocation heuristics in this environment

Over the past several years, a heterogeneous computing (HC) system has become more competative as a commercial computing platform than a homogeneous system. With the growing scale of HC systems, network failures become inevitable. To achieve high performance, communication reliability should be considered while designing reliability-aware task scheduling algorithms. In this paper, we propose a new algorithm called RMSR (Replication-based scheduling for Maximizing System Reliability), which incorporates task communication into system reliability. To maximize communication reliability, an improved algorithm which searches all optimal reliability communication paths for current tasks is proposed. During the task replication phase, the task reliability threshold is determined by users and each task has dynamic replicas. Our comparative studies based on randomly generated graphs show that our RMSR algorithm outperforms existing scheduling algorithms in terms of system reliability. Several factors affecting the performance are analyzed in the paper.

In an effort to provide a common platform for utilizing and developing data analysis and assessment applications in heterogeneous high performance computing (HPC) environments, the Classified Data Analysis and Assessment Center (CDAAC) has developed the computational science environment (CSE). CSE provides a stable suite of data analysis and assessment tools, applications, and libraries, which are common across most HPC environments and standard 64-bit Linux workstations. CSE also provides an experimental, cutting-edge suite of developer tools that can enhance the data analysis and assessment process. Most importantly, CSE provides a common platform that enables the development of full-featured portable HPC applications. This paper provides an overview of how CSE works and highlights some of the benefits of CSE.

article Free Access Share on Software support for heterogeneous computing Authors: Howard Jay Siegel Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , Henry G. Dietz Purdue University, West Lafayette, Indiana Purdue University, West Lafayette, IndianaView Profile , John K. Antonio Texas Tech University, Lubbock Texas Tech University, LubbockView Profile Authors Info & Claims ACM Computing SurveysVolume 28Issue 1March 1996 pp 237–239https://doi.org/10.1145/234313.234411Published:01 March 1996Publication History 21citation711DownloadsMetricsTotal Citations21Total Downloads711Last 12 Months67Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In a dedicated, mixed-machine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC Is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.

As the size and complexity of high performance computing (HPC) systems grow in line with advancements in hardware and software technology, HPC systems increasingly suffer from performance variations due to shared resource contention as well as software- and hardware-related problems. Such performance variations can lead to failures and inefficiencies, which impact the cost and resilience of HPC systems. To minimize the impact of performance variations, one must quickly and accurately detect and diagnose the anomalies that cause the variations and take mitigating actions. However, it is difficult to identify anomalies based on the voluminous, high-dimensional, and noisy data collected by system monitoring infrastructures. This paper presents a novel machine learning based framework to automatically diagnose performance anomalies at runtime. Our framework leverages historical resource usage data to extract signatures of previously-observed anomalies. We first convert collected time series data into easy-to-compute statistical features. We then identify the features that are required to detect anomalies, and extract the signatures of these anomalies. At runtime, we use these signatures to diagnose anomalies with negligible overhead. We evaluate our framework using experiments on a real-world HPC supercomputer and demonstrate that our approach successfully identifies 98 percent of injected anomalies and consistently outperforms existing anomaly diagnosis techniques.

As an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.

Parallel discrete event simulation (PDES) is an important application in use in many DoD projects; for example, PDES is used in large-scale war-gaming, and in complex system design, analysis and verification. Improving PDES performance and capacity allows faster simulation times and more extensive analysis of more detailed models. These benefits are not application-specific: they should reflect to any application that uses the improved simulation kernel. In this work, we overview our efforts for optimizing PDES in a heterogeneous high performance computing (HHPC) environment. We profile the SPEEDES simulator and identify several opportunities. We report on our experiences on two fronts: (1) optimizing the communication subsystem - a critical system for PDES since it is a fine-grained application and (2) exploring the use of augmented FPGA boards to accelerate simulation. While such approaches have been attempted for sequential and data path intensive applications, we believe that their use in clustered environments is novel. Both efforts are works in progress; we present our designs and some preliminary analysis results. For example, removing the centralized communication server from event message exchange path with a number of other small improvements to the simulation cycle, improved performance by an average of 20% performance improvement for one of our large benchmarks.

Multi-physics simulations are at the heart of today's engineering applications. The trend is towards more realistic and detailed simulations, which demand highly resolved spatial and temporal scales of various physical mechanisms to solve engineering problems in a reasonable amount of time. As a consequence, numerical codes need to run efficiently on high-performance computers. Therefore, the framework Zonal Flow Solver (ZFS) featuring lattice-Boltzmann, finite-volume, discontinuous Galerkin, level set and Lagrange solvers has been developed. The solvers can be combined to simulate, e.g. quasi-incompressible and compressible flow, aeroacoustics, moving boundaries and particle dynamics. In this manuscript, the multi-physics implementation of the coupling mechanisms are presented. The parallelisation approach, the involved solvers and their scalability on state-of-the-art heterogeneous high-performance computers are discussed. Various multi-physics applications complement the discussion. The results show ZFS to be a highly efficient and flexible multi-purpose tool that can be used to solve varying classes of coupled problems.

A stochastic Petri net (SPN) is systematically constructed from a task graph whose component subtasks are statically allocated onto the processor suite of a heterogeneous computing system (HCS). Given that subtask execution times are exponentially distributed an exponential distribution can be generated for the overall completion time. In particular the enabling functions and rate functions used to specify the SPN model provide needed versatility to integrate processor heterogeneity, task priorities, allocation schemes, communication costs, and other factors characteristic of a HCS into a comprehensive performance analysis. The manner in which these parameters are incorporated into the SPN allows the model to be transformed into a testbed for optimization schemes and heuristics. The proposed approach can be applied to arbitrary task graphs including non-series-parallel.

This research is an effort to develop a Risk Management‐based Decision Analysis (RMDA) framework based on the common fundamental elements that define the nature of resilience in Maritime Infrastructure and Transportation Systems (MITS). The framework develops a systemic approach to the decision making process in regards to investing on resilience strategies. It also enables stakeholders and decision makers of maritime systems to identify, analyze, and prioritize risks involved in MITS operations; to define ways for risk mitigation, plan for contingencies, and devise mechanisms for continuously monitoring and controlling risk factors and threats to the system; and to value the adopted resilience investment plans and strategies. Our suggested RMDA framework is developed based on the Six‐element Flexibility Framework, which is offered for assessing the value of flexibility strategies in Space Systems, and utilizes a Decision Tree Analysis (DTA) methodology for assessing the cost‐effectiveness of the devised strategies.

Heterogeneous computing has emerged as a promising fit for scientific domains such as molecular dynamics simulations, bioinformatics, weather prediction. Such a computing paradigm includes x86 processors coupled with GPUs, FPGAs, DSPs or a coprocessor paradigm that takes advantage of all the cores and caches on a single die such as the Knights Landing. OpenACC, a high-level directive-based parallel programming model has emerged as a programming paradigm that can tackle the intensity of heterogeneity in architectures. Data-driven large scientific codes are increasingly using OpenACC, which makes it essential to analyze the accuracy of OpenACC compilers while they port code to various types of platforms. In response, we have been creating a validation suite to validate and verify the implementations of OpenACC features in conformance with the specification. The validation suite also provides a tool to compiler developers as a standard for the compiler to be tested against and to users and compiler developers alike in clarifying the OpenACC specification. This testsuite has been integrated into the harness infrastructure of the TITAN and Summitdev systems at Oak Ridge National Lab and is being used for production.