The number of processors embedded on high performance computing platforms is continuously increasing to accommodate user desire to solve larger and more complex problems. However, as the number of components increases, so does the probability of failure. Thus, both scalable and fault-tolerance of software are important issues in this field. To ensure reliability of the software especially under the failure circumstance, the reliability analysis is needed. The discrete-event simulation technique offers an attractive a ternative to traditional Markovian-based analytical models, which often have an intractably large state space. In this paper, we analyze reliability of a self-healing network developed for parallel runtime environments using discrete-event simulation. The network is designed to support transmission of messages across multiple nodes and at the same time, to protect against node and process failures. Results demonstrate the flexibility of a discrete-event simulation approach for studying the network behavior under failure conditions and various protocol parameters, message types, and routing algorithms.

With the increasing number of processors in modern HPC(High Performance Computing) systems, there are two emergent problems to solve. One is scalability, the other is fault tolerance. In our previous work, we extended the MPI specification on handling fault tolerance by specifying a systematic framework for the recovery methods, communicator, message modes etc. that define the behavior of MPI in case an error occurs. These extensions not only specify how the implementation of the MPI library and RTE (Run Time Environment) handle failures at the system level, but provide the normal HPC application developers with various recovery choices with varying performance and cost. In this paper, we continue the work on extending the MPI’s capability in this direction. Firstly, we are proposing an MPI operation level checkpoint/rollback library to recover the user’s data. More importantly, we argue that the future generation programming model of a fault tolerant MPI application should be recover-and-continue against the more traditional stop-and-restart model. Recover-and-continue means that in case an error occurs, we just re-spawn the failed processes. All the remaining living processes stay in their original processors mapping on memory. The main benefits of recover-and-continue are much less cost for system recovery and the opportunity of employing in-memory checkpoint/ rollback techniques. Compared with stable or local disk techniques, which are the only choices for stop-andrestart, doubtlessly, the in-memory approach significantly reduces the performance penalty in checkpoint/rollback. Additionally, it makes it possible to establish a concurrent multiple level checkpoint/ rollback framework. With the progress of our work, a picture of the hierarchy of future generation fault tolerant HPC system will be gradually unveiled.

As the number of processors in today's high performance computers continues to grow, the mean-time-to-failure of these computers are becoming significantly shorter than the execution time of many current high performance computing applications. Although today's architectures are usually robust enough to survive node failures without suffering complete system failure, most today's high performance computing applications can not survive node failures and, therefore, whenever a node fails, have to abort themselves and restart from the beginning or a stable-storage-based checkpoint.This paper explores the use of the floating-point arithmetic coding approach to build fault survivable high performance computing applications so that they can adapt to node failures without aborting themselves. Despite the use of erasure codes over Galois field has been theoretically attempted before in diskless checkpointing, few actual implementations exist. This probably derives from concerns related to both the efficiency and the complexity of implementing such codes in high performance computing applications. In this paper, we introduce the simple but efficient floating-point arithmetic coding approach into diskless checkpointing and address the associated round-off error issue. We also implement a floating-point arithmetic version of the Reed-Solomon coding scheme into a conjugate gradient equation solver and evaluate both the performance and the numerical impact of this scheme. Experimental results demonstrate that the proposed floating-point arithmetic coding approach is able to survive a small number of simultaneous node failures with low performance overhead and little numerical impact.

In this work we present the Internet Backplane Protocol (IBP), a middleware created to allow the sharing of storage resources, implemented as part of the network fabric. IBP allows an application to control intermediate data staging operations explicitly. As IBP follows a very simple philosophy, very similar to the Internet Protocol, and the resulting semantic might be too weak for some applications, we introduce the exNode, a data structure that aggregates storage allocations on the Internet.

This paper describes an overall framework for the design of numerical libraries on a computational Grid of processors where the processors may be geographically distributed and under the control of a Grid-based scheduling system. A set of experiments are presented in the context of solving systems of linear equations using routines from the ScaLAPACK software collection along with various grid service components, such as Globus, NWS, and Autopilot.

The performance of the MPI's collective communications is critical in most MPI-based applications. A general algorithm for a given collective communication operation may not give good performance on all systems due to the differences in architectures, network parameters and the storage capacity of the underlying MPI implementation. In this paper, we discuss an approach in which the collective communications are tuned for a given system by conducting a series of experiments on the system. We also discuss a dynamic topology method that uses the tuned static topology shape, but re-orders the logical addresses to compensate for changing run time variations. A series of experiments were conducted comparing our tuned collective communication operations to various native vendor MPI implementations. The use of the tuned collective communications resulted in about 30%-650% improvement in performance over the native MPI implelementations.

We describe our vision, goals and plans for HARNESS, a distributed, reconfigurable and heterogeneous computing environment that supports dynamically adaptable parallel applications. HARNESS builds on the core concept of the personal virtual machine as an abstraction for distributed parallel programming, but fundamentally extends this idea, greatly enhancing dynamic capabilities. HARNESS is being designed to embrace dynamics at every level through a pluggable model that allows multiple distributed virtual machines (DVMs) to merge, split and interact with each other. It provides mechanisms for new and legacy applications to collaborate with each other using the HARNESS infrastructure, and defines and implements new plug-in interfaces and modules so that applications can dynamically customize their virtual environment. HARNESS fits well within the larger picture of computational grids as a dynamic mechanism to hide the heterogeneity and complexity of the nationally distributed infrastructure. HARNESS DVMs allow programmers and users to construct personal subsets of an existing computational grid and treat them as unified network computers, providing a familiar and comfortable environment that provides easy-to-understand scoping.

SNIPE is a metacomputing system that aims to provide a reliable, secure, fault-tolerant environment for long-term distributed computing applications and data stores across the global InterNet. This system combines global naming and replication of both processing and data to support large scale information processing applications leading to better availablity and reliability than currently available with typical cluster computing and/or distributed computer environments.