10,295 research outputs found
CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance
In order to efficiently use the future generations of supercomputers, fault
tolerance and power consumption are two of the prime challenges anticipated by
the High Performance Computing (HPC) community. Checkpoint/Restart (CR) has
been and still is the most widely used technique to deal with hard failures.
Application-level CR is the most effective CR technique in terms of overhead
efficiency but it takes a lot of implementation effort. This work presents the
implementation of our C++ based library CRAFT (Checkpoint-Restart and Automatic
Fault Tolerance), which serves two purposes. First, it provides an extendable
library that significantly eases the implementation of application-level
checkpointing. The most basic and frequently used checkpoint data types are
already part of CRAFT and can be directly used out of the box. The library can
be easily extended to add more data types. As means of overhead reduction, the
library offers a build-in asynchronous checkpointing mechanism and also
supports the Scalable Checkpoint/Restart (SCR) library for node level
checkpointing. Second, CRAFT provides an easier interface for User-Level
Failure Mitigation (ULFM) based dynamic process recovery, which significantly
reduces the complexity and effort of failure detection and communication
recovery mechanism. By utilizing both functionalities together, applications
can write application-level checkpoints and recover dynamically from process
failures with very limited programming effort. This work presents the design
and use of our library in detail. The associated overheads are thoroughly
analyzed using several benchmarks
Algorithmic Based Fault Tolerance Applied to High Performance Computing
We present a new approach to fault tolerance for High Performance Computing
system. Our approach is based on a careful adaptation of the Algorithmic Based
Fault Tolerance technique (Huang and Abraham, 1984) to the need of parallel
distributed computation. We obtain a strongly scalable mechanism for fault
tolerance. We can also detect and correct errors (bit-flip) on the fly of a
computation. To assess the viability of our approach, we have developed a fault
tolerant matrix-matrix multiplication subroutine and we propose some models to
predict its running time. Our parallel fault-tolerant matrix-matrix
multiplication scores 1.4 TFLOPS on 484 processors (cluster jacquard.nersc.gov)
and returns a correct result while one process failure has happened. This
represents 65% of the machine peak efficiency and less than 12% overhead with
respect to the fastest failure-free implementation. We predict (and have
observed) that, as we increase the processor count, the overhead of the fault
tolerance drops significantly
A proactive fault tolerance framework for high performance computing (HPC) systems in the cloud
High Performance Computing (HPC) systems have been widely used by scientists and researchers in both industry and university laboratories to solve advanced computation problems. Most advanced computation problems are either data-intensive or computation-intensive. They may take hours, days or even weeks to complete execution. For example, some of the traditional HPC systems computations run on 100,000 processors for weeks. Consequently traditional HPC systems often require huge capital investments. As a result, scientists and researchers sometimes have to wait in long queues to access shared, expensive HPC systems. Cloud computing, on the other hand, offers new computing paradigms, capacity, and flexible solutions for both business and HPC applications. Some of the computation-intensive applications that are usually executed in traditional HPC systems can now be executed in the cloud. Cloud computing price model eliminates huge capital investments. However, even for cloud-based HPC systems, fault tolerance is still an issue of growing concern. The large number of virtual machines and electronic components, as well as software complexity and overall system reliability, availability and serviceability (RAS), are factors with which HPC systems in the cloud must contend. The reactive fault tolerance approach of checkpoint/restart, which is commonly used in HPC systems, does not scale well in the cloud due to resource sharing and distributed systems networks. Hence, the need for reliable fault tolerant HPC systems is even greater in a cloud environment. In this thesis we present a proactive fault tolerance approach to HPC systems in the cloud to reduce the wall-clock execution time, as well as dollar cost, in the presence of hardware failure. We have developed a generic fault tolerance algorithm for HPC systems in the cloud. We have further developed a cost model for executing computation-intensive applications on HPC systems in the cloud. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in the cloud can be considerably reduced compared to checkpoint and redundancy techniques used in traditional HPC systems
Checkpointing of parallel applications in a Grid environment
The Grid environment is generic, heterogeneous, and dynamic with lots of unreliable resources making it very exposed to failures. The environment is unreliable because it is geographically dispersed involving multiple autonomous administrative domains and it is composed of a large number of components. Examples of failures in the Grid
environment can be: application crash, Grid node crash, network failures, and Grid system component failures. These types of failures can affect the execution of
parallel/distributed application in the Grid environment and so, protections against these faults are crucial. Therefore, it is essential to develop efficient fault tolerant mechanisms to allow users to successfully execute Grid applications. One of the research challenges in Grid computing is to be able to develop a fault tolerant solution that will ensure Grid applications are executed reliably with minimum overhead incurred.
While checkpointing is the most common method to achieve fault tolerance, there is still a lot of work to be done to improve the efficiency of the mechanism. This thesis provides an in-depth description of a novel solution for checkpointing parallel applications executed on a Grid. The checkpointing mechanism implemented allows to checkpoint an application at regions where there is no interprocess communication involved and therefore reducing the checkpointing overhead and checkpoint size
Computing at massive scale: Scalability and dependability challenges
Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications
Grid simulation services for the medical community
The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services
NASA/NBS (National Aeronautics and Space Administration/National Bureau of Standards) standard reference model for telerobot control system architecture (NASREM)
The document describes the NASA Standard Reference Model (NASREM) Architecture for the Space Station Telerobot Control System. It defines the functional requirements and high level specifications of the control system for the NASA space Station document for the functional specification, and a guideline for the development of the control system architecture, of the 10C Flight Telerobot Servicer. The NASREM telerobot control system architecture defines a set of standard modules and interfaces which facilitates software design, development, validation, and test, and make possible the integration of telerobotics software from a wide variety of sources. Standard interfaces also provide the software hooks necessary to incrementally upgrade future Flight Telerobot Systems as new capabilities develop in computer science, robotics, and autonomous system control
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