ROSE::FTTransform - A source-to-source translation framework for exascale fault-tolerance research
Paper i proceeding, 2012

Exascale computing systems will require sufficient resilience to tolerate numerous types of hardware faults while still assuring correct program execution. Such extreme-scale machines are expected to be dominated by processors driven at lower voltages (near the minimum 0.5 volts for current transistors). At these voltage levels, the rate of transient errors increases dramatically due to the sensitivity to transient and geographically localized voltage drops on parts of the processor chip. To achieve power efficiency, these processors are likely to be streamlined and minimal, and thus they cannot be expected to handle transient errors entirely in hardware. Here we present an open, compiler-based framework to automate the armoring of High Performance Computing (HPC) software to protect it from these types of transient processor errors. We develop an open infrastructure to support research work in this area, and we define tools that, in the future, may provide more complete automated and/or semi-automated solutions to support software resiliency on future exascale architectures. Results demonstrate that our approach is feasible, pragmatic in how it can be separated from the software development process, and reasonably efficient (0% to 30% overhead for the Jacobi iteration on common hardware; and 20%, 40%, 26%, and 2% overhead for a randomly selected subset of benchmarks from the Livermore Loops [1]).

Fault Tolerance

High Performance Computing

Exascale

Source-to-Source Compiler

Redundancy

Författare

Jacob Lidman

Chalmers, Data- och informationsteknik, Datorteknik

D. J. Quinlan

Lawrence Livermore National Laboratory

C. Liao

Lawrence Livermore National Laboratory

Sally A McKee

Chalmers, Data- och informationsteknik, Datorteknik

IEEE/IFIP 42nd International Conference on Dependable Systems and Networks Workshops, DSN-W 2012; Boston, MA; United States; 25 June 2012 through 28 June 2012

6264672

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1109/DSNW.2012.6264672

ISBN

978-146732264-5

Mer information

Skapat

2017-10-07