Publications

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As High-End Computing machines continue to grow in size, issues such as fault tolerance and reliability limit application scalability. Current techniques to ensure progress across faults, like checkpoint-restart, are unsuitable at these scale due to excessive overheads predicted to more than double an applications time to solution. Redundant computation, long used in distributed and mission critical systems, has been suggested as an alternative to checkpoint-restart on its own. In this paper we describe the rMPI library which enables portable and transparent redundant computation for MPI applications. We detail the design of the library as well as two replica consistency protocols, outline the overheads of this library at scale on a number of real-world applications, and finally outline the significant increase in an applications time to solution at extreme scale as well as show the scenarios in which redundant computation makes sense.

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Exascale systems will have hundred thousands of compute nodes and millions of components which increases the likelihood of faults. Today, applications use checkpoint/restart to recover from these faults. Even under ideal conditions, applications running on more than 50,000 nodes will spend more than half of their total running time saving checkpoints, restarting, and redoing work that was lost. Redundant computing is a method that allows an application to continue working even when failures occur. Instead of each failure causing an application interrupt, multiple failures can be absorbed by the application until redundancy is exhausted. In this paper we present a method to analyze the benefits of redundant computing, present simulation results of the cost, and compare it to other proposed methods for fault resilience.

The two primary objectives of this LDRD project were to create a lightweight kernel (LWK) operating system(OS) designed to take maximum advantage of multi-core processors, and to leverage the virtualization capabilities in modern multi-core processors to create a more flexible and adaptable LWK environment. The most significant technical accomplishments of this project were the development of the Kitten lightweight kernel, the co-development of the SMARTMAP intra-node memory mapping technique, and the development and demonstration of a scalable virtualization environment for HPC. Each of these topics is presented in this report by the inclusion of a published or submitted research paper. The results of this project are being leveraged by several ongoing and new research projects.

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Palacios is a new open-source VMM under development at Northwestern University and the University of New Mexico that enables applications executing in a virtualized environment to achieve scalable high performance on large machines. Palacios functions as a modularized extension to Kitten, a high performance operating system being developed at Sandia National Laboratories to support large-scale supercomputing applications. Together, Palacios and Kitten provide a thin layer over the hardware to support full-featured virtualized environments alongside Kitten's lightweight native environment. Palacios supports existing, unmodified applications and operating systems by using the hardware virtualization technologies in recent AMD and Intel processors. Additionally, Palacios leverages Kitten's simple memory management scheme to enable low-overhead pass-through of native devices to a virtualized environment. We describe the design, implementation, and integration of Palacios and Kitten. Our benchmarks show that Palacios provides near native (within 5%), scalable performance for virtualized environments running important parallel applications. This new architecture provides an incremental path for applications to use supercomputers, running specialized lightweight host operating systems, that is not significantly performance-compromised. © 2010 IEEE.

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Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require a significant advancements in several fundamental areas. Recent studies have outlined many of the challenges in hardware and software that will be needed. In this paper, we examine these challenges with respect to high-performance networking. We describe the repercussions of anticipated changes to computing and networking hardware and discuss the impact that alternative parallel programming models will have on the network software stack. We also present some ideas on possible approaches that address some of these challenges.

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Extreme-scale parallel systems will require alternative methods for applications to maintain current levels of uninterrupted execution. Redundant computation is one approach to consider, if the benefits of increased resiliency outweigh the cost of consuming additional resources. We describe a transparent redundancy approach for MPI applications and detail two different implementations that provide the ability to tolerate a range of failure scenarios, including loss of application processes and connectivity.We compare these two approaches and show performance results from micro-benchmarks that bound worst-case message passing performance degradation.We propose several enhancements that could lower the overhead of providing resiliency through redundancy.

There is considerable interest in achieving a 1000 fold increase in supercomputing power in the next decade, but the challenges are formidable. In this paper, the authors discuss some of the driving science and security applications that require Exascale computing (a million, trillion operations per second). Key architectural challenges include power, memory, interconnection networks and resilience. The paper summarizes ongoing research aimed at overcoming these hurdles. Topics of interest are architecture aware and scalable algorithms, system simulation, 3D integration, new approaches to system-directed resilience and new benchmarks. Although significant progress is being made, a broader international program is needed.

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This paper presents a parallel programming model, Parallel Phase Model (PPM), for next-generation high-end parallel machines based on a distributed memory architecture consisting of a networked cluster of nodes with a large number of cores on each node. PPM has a unified high-level programming abstraction that facilitates the design and implementation of parallel algorithms to exploit both the parallelism of the many cores and the parallelism at the cluster level. The programming abstraction will be suitable for expressing both fine-grained and coarse-grained parallelism. It includes a few high-level parallel programming language constructs that can be added as an extension to an existing (sequential or parallel) programming language such as C; and the implementation of PPM also includes a light-weight runtime library that runs on top of an existing network communication software layer (e.g. MPI). Design philosophy of PPM and details of the programming abstraction are also presented. Several unstructured applications that inherently require high-volume random fine-grained data accesses have been implemented in PPM with very promising results. © 2009 IEEE.

Petaflops systems will have tens to hundreds of thousands of compute nodes which increases the likelihood of faults. Applications use checkpoint/restart to recover from these faults, but even under ideal conditions, applications running on more than 30,000 nodes will likely spend more than half of their total run time saving checkpoints, restarting, and redoing work that was lost. We created a library that performs redundant computations on additional nodes allocated to the application. An active node and its redundant partner form a node bundle which will only fail, and cause an application restart, when both nodes in the bundle fail. The goal of this library is to learn whether this can be done entirely at the user level, what requirements this library places on a Reliability, Availability, and Serviceability (RAS) system, and what its impact on performance and run time is. We find that our redundant MPI layer library imposes a relatively modest performance penalty for applications, but that it greatly reduces the number of applications interrupts. This reduction in interrupts leads to huge savings in restart and rework time. For large-scale applications the savings compensate for the performance loss and the additional nodes required for redundant computations.

Palacios and Kitten are new open source tools that enable applications, whether ported or not, to achieve scalable high performance on large machines. They provide a thin layer over the hardware to support both full-featured virtualized environments and native code bases. Kitten is an OS under development at Sandia that implements a lightweight kernel architecture to provide predictable behavior and increased flexibility on large machines, while also providing Linux binary compatibility. Palacios is a VMM that is under development at Northwestern University and the University of New Mexico. Palacios, which can be embedded into Kitten and other OSes, supports existing, unmodified applications and operating systems by using virtualization that leverages hardware technologies. We describe the design and implementation of both Kitten and Palacios. Our benchmarks show that they provide near native, scalable performance. Palacios and Kitten provide an incremental path to using supercomputer resources that is not performance-compromised.