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Stearley, Jon R

Publications and source records attributed to Stearley, Jon R.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

SNL software manual for the ACS Data Analytics Project.

In the ACS Data Analytics Project (also known as 'YumYum'), a supercomputer is modeled as a graph of components and dependencies, jobs and faults are simulated, and component fault rates are estimated using the graph structure and job pass/fail outcomes. This report documents the successful completion of all SNL deliverables and tasks, describes the software written by SNL for the project, and presents the data it generates. Readers should understand what the software tools are, how they fit together, and how to use them to reproduce the presented data and additional experiments as desired. The SNL YumYum tools provide the novel simulation and inference capabilities desired by ACS. SNL also developed and implemented a new algorithm, which provides faster estimates, at finer component granularity, on arbitrary directed acyclic graphs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Keeping checkpoint/restart viable for exascale systems.

Next-generation exascale systems, those capable of performing a quintillion (10{sup 18}) operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoint) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

rMPI : increasing fault resiliency in a message-passing environment.

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.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2010

Redundant computing for exascale systems.

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.

99 GENERAL AND MISCELLANEOUS↗