Large-scale Heterogeneous Cluster Research Articles

Toward fault-tolerant hybrid programming over large-scale heterogeneous clusters via checkpointing/restart optimization

Massively heterogeneous architectures are widely adopted for the design of modern peta-scale and future exa-scale systems. In such heterogeneous clusters, due to the increasing number of involved components, it is essential to enable fault tolerance to improve the reliability of the whole system. However, existing programming models for heterogeneous clusters (e.g., MPI $$+$$ X) concern more on performance, instead of reliability. In this paper, we design and implement a fault tolerance framework for hybrid programs that leverage heterogeneous hardware architectures based on the in-memory checkpointing technique. We provide new capabilities for programming heterogeneous applications that can greatly simplify the implementation of application-level checkpointing. We also conduct optimizations on checkpoint saving and loading to increase scalability. We validate effectiveness of the framework with various benchmarks and real-world applications on the Tianhe-2 supercomputer. Our experimental results show that our framework can improve the resilience of long-running applications and reduce checkpointing overhead.

An effective reliability-driven technique of allocating tasks on heterogeneous cluster systems

In large-scale heterogeneous cluster computing systems, processor and network failures are inevitable and can have an adverse effect on applications executing on such systems. One way of taking failures into account is to employ a reliable scheduling algorithm. However, most existing scheduling algorithms for precedence constrained tasks in heterogeneous systems only consider scheduling length, and not efficiently satisfy the reliability requirements of task. In recognition of this problem, we build an application reliability analysis model based on Weibull distribution, which can dynamically measure the reliability of task executing on heterogeneous cluster with arbitrary networks architectures. Then, we propose a reliability-driven earliest finish time with duplication scheduling algorithm (REFTD) which incorporates task reliability overhead into scheduling. Furthermore, to improve system reliability, it duplicates task as if task hazard rate is more than threshold $$\theta $$ ? . The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithm can shorten schedule length and improve system reliability significantly.

Jcluster: an efficient Java parallel environment on a large-scale heterogeneous cluster

In this paper, we present Jcluster, an efficient Java parallel environment that provides some critical services, in particular automatic load balancing and high-performance communication, for developing parallel applications in Java on a large-scale heterogeneous cluster. In the Jcluster environment, we implement a task scheduler based on a transitive random stealing (TRS) algorithm. Performance evaluations show that the scheduler based on TRS can make any idle node obtain a task from another node with much fewer stealing times than random stealing (RS), which is a well-known dynamic load-balancing algorithm, on a large-scale cluster. In the performance aspects of communication, with the method of asynchronously multithreaded transmission, we implement a high-performance PVM-like and MPI-like message-passing interface in pure Java. The evaluation of the communication performance is conducted among the Jcluster environment, LAM-MPI and mpiJava on LAM-MPI based on the Java Grande Forum's pingpong benchmark. Copyright © 2005 John Wiley & Sons, Ltd.

Simulative performance analysis of gossip failure detection for scalable distributed systems

Three protocols for gossip-based failure detection services in large-scale heterogeneous clusters are analyzed and compared. The basic gossip protocol provides a means by which failures can be detected in large distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. The hierarchical protocol leverages the underlying network topology to achieve faster failure detection. In addition to studying the effectiveness and efficiency of these two agreement protocols, we propose a third protocol that extends the hierarchical approach by piggybacking gossip information on application-generated messages. The protocols are simulated and evaluated with a fault-injection model for scalable distributed systems comprised of clusters of workstations connected by high-performance networks, such as the CPlant system at Sandia National Laboratories. The model supports permanent and transient node and link failures, with rates specified at simulation time, for processors functioning in a fail-silent fashion. Through high-fidelity, CAD-based modeling and simulation, we demonstrate the strengths and weaknesses of each approach in terms of agreement time, number of gossips, and overall scalability.