Malleable applications for scalable high performance computing

2006

This paper describes a modular decentralized middleware framework for dynamic application reconfiguration in large scale heterogeneous environments. Component malleability is presented as a dynamic reconfiguration strategy. Dynamic component granularity enables improved load balancing by component migration, and most importantly, it enables applications to scale up to arbitrarily large numbers of processing nodes in a relatively transparent way. Preliminary experimental results show that without significant overhead, malleable components can improve applications performance and distributed resource utilization.

Applied Sciences

Maintaining a high rate of productivity, in terms of completed jobs per unit of time, in High-Performance Computing (HPC) facilities is a cornerstone in the next generation of exascale supercomputers. Process malleability is presented as a straightforward mechanism to address that issue. Nowadays, the vast majority of HPC facilities are intended for distributed-memory applications based on the Message Passing (MP) paradigm. For this reason, many efforts are based on the Message Passing Interface (MPI), the de facto standard programming model. Malleability aims to rescale executions on-the-fly, in other words, reconfigure the number and layout of processes in running applications. Process malleability involves resources reallocation within the HPC system, handling processes of the application, and redistributing data among those processes to resume the execution. This manuscript compiles how different frameworks address process malleability, their main features, their integration in ...

2005

Computational grids are appealing platforms for the execution of large scale applications among the scientific and engineering communities. However, designing new applications and deploying existing ones with the capability of exploiting this potential still remains a challenge. Computational grids are characterized by their dynamic, non-dedicated, and heterogeneous nature. Novel application-level and middleware-level techniques are needed to allow applications to reconfigure themselves and adapt automatically to their underlying execution environments. In this paper, we introduce a new software framework that enhances the performance of Message Passing Interface (MPI) applications through an adaptive middleware for load balancing that includes process checkpointing and migration. Fields as diverse as fluid dynamics, materials science, biomechanics, and ecology make use of parallel adaptive computation. Target architectures have traditionally been supercomputers and tightly coupled clusters. This framework is a first step in allowing these computations to use computational grids efficiently.

Parallel Computing, 2015

The work in this paper focuses on providing malleability to MPI applications by using a novel performance-aware dynamic reconfiguration technique. This paper describes the design and implementation of Flex-MPI, an MPI library extension which can automatically monitor and predict the performance of applications, balance and redistribute the workload, and reconfigure the application at runtime by changing the number of processes. Unlike existent approaches, our reconfiguring policy is guided by user-defined performance criteria. We focus on iterative SPMD programs, a class of applications with critical mass within the scientific community. Extensive experiments show that Flex-MPI can improve the performance, parallel efficiency, and cost-efficiency of MPI programs with a minimal effort from the programmer.

2001

Current tools available for high performance computing require that all the computing nodes used in a parallel execution be known in advance: the execution environment must know where the different "chunks" of programs will be executed, and each computer involved in the execution must be properly configured. In this paper, we describe how the Web Operating System (WOS TM) environment may be used to dynamically locate available computers to perform such computations and how these computers are dynamically configured. The WOS TM (?) is a virtual operating system which is suitable for supporting and managing distributed/parallel processing on the Internet. Central to the WOS TM architecture are the communication protocols, which may be seen as the "glue" of the whole environment. Communication between nodes is realized through a generic service protocol and a simple discovery/location protocol (?). The service protocol may be versioned to support specialized services. In the present research, we focus on the design of such a version for High Performance computing. This version essentially locates nodes able to execute parallel programs, identifies nodes available for participating in the parallel execution, and sets up an execution environment on the dynamically selected set of nodes.

Lecture Notes in Computer Science, 2004

Many scientific applications require computational capabilities not easily supported by current computing environments. We propose a scalable computing environment based on autonomous actors. In this approach, a wide range of computational resources, ranging from clusters to desktops and laptops, can run an application programmed using actors as program components in an actor language: SALSA. SALSA actors have the ability to execute autonomously in dynamically reconfigurable computing environments. We develop the corresponding "Internet Operating system" (IO) to address run-time middleware issues such as permanent storage for results produced by actors, inter-actor communication and synchronization, and fault-tolerance in a manner transparent to the end-user. We are using this worldwide computing software infrastructure to solve a long outstanding problem in particle physics: the missing baryons, originally identified over thirty years ago.

Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07), 2007

Malleability enables a parallel application's execution system to split or merge processes modifying granularity. While process migration is widely used to adapt applications to dynamic execution environments, it is limited by the granularity of the application's processes. Malleability empowers process migration by allowing the application's processes to expand or shrink following the availability of resources. We have implemented malleability as an extension to the PCM (Process Checkpointing and Migration) library, a user-level library for iterative MPI applications. PCM is integrated with the Internet Operating System (IOS), a framework for middleware-driven dynamic application reconfiguration. Our approach requires minimal code modifications and enables transparent middleware-triggered reconfiguration. Experimental results using a two-dimensional data parallel program that has a regular communication structure demonstrate the usefulness of malleability.

2007

for middleware-driven dynamic application reconfiguration. Our approach requires minimal code modifications and enables transparent middleware-triggered reconfiguration. We present experimental results that demonstrate the usefulness of malleability.

Concurrency and Computation: Practice and Experience, 2009

Malleability enables a parallel application's execution system to split or merge processes modifying granularity. While process migration is widely used to adapt applications to dynamic execution environments, it is limited by the granularity of the application's processes. Malleability empowers process migration by allowing the application's processes to expand or shrink following the availability of resources. We have implemented malleability as an extension to the process checkpointing and migration (PCM) library, a user-level library for iterative message passing interface (MPI) applications. PCM is integrated with the Internet Operating System, a framework for middleware-driven dynamic application reconfiguration. Our approach requires minimal code modifications and enables transparent middleware-triggered reconfiguration. Experimental results using a two-dimensional data parallel program that has a regular communication structure demonstrate the usefulness of malleability.

2008

Abstract Computational grids are characterized by their dynamic, non-dedicated, and heterogeneous nature. Novel application-level and middleware-level techniques are needed to allow applications to reconfigure themselves and adapt automatically to their underlying execution environments to be able to benefit from computational grids' resources.