Cross-layer system reliability assessment framework for hardware faults

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft

Statistical Reliability Estimation of Microprocessor-Based Systems

What is the probability that the execution state of a given microprocessor running a given application is correct, in a certain working environment with a given soft-error rate? Trying to answer this question using fault injection can be very expensive and time consuming. This paper proposes the baseline for a new methodology, based on microprocessor error probability profiling, that aims at estimating fault injection results without the need of a typical fault injection setup. The proposed methodology is based on two main ideas: a one-time fault-injection analysis of the microprocessor architecture to characterize the probability of successful execution of each of its instructions in presence of a soft-error, and a static and very fast analysis of the control and data flow of the target software application to compute its probability of success. The presented work goes beyond the dependability evaluation problem; it also has the potential to become the backbone for new tools able to help engineers to choose the best hardware and software architecture to structurally maximize the probability of a correct execution of the target softwar

Evaluating the Impact of SDC on the GMRES Iterative Solver

Increasing parallelism and transistor density, along with increasingly tighter energy and peak power constraints, may force exposure of occasionally incorrect computation or storage to application codes. Silent data corruption (SDC) will likely be infrequent, yet one SDC suffices to make numerical algorithms like iterative linear solvers cease progress towards the correct answer. Thus, we focus on resilience of the iterative linear solver GMRES to a single transient SDC. We derive inexpensive checks to detect the effects of an SDC in GMRES that work for a more general SDC model than presuming a bit flip. Our experiments show that when GMRES is used as the inner solver of an inner-outer iteration, it can "run through" SDC of almost any magnitude in the computationally intensive orthogonalization phase. That is, it gets the right answer using faulty data without any required roll back. Those SDCs which it cannot run through, get caught by our detection scheme

Determining the Structure of Supersymmetry-Breaking with Renormalization Group Invariants

If collider experiments demonstrate that the Minimal Supersymmetric Standard Model (MSSM) is a good description of nature at the weak scale, the experimental priority will be the precise determination of superpartner masses. These masses are governed by the weak scale values of the soft supersymmetry (SUSY)-breaking parameters, which are in turn highly dependent on the SUSY-breaking scheme present at high scales. It is therefore of great interest to find patterns in the soft parameters that can distinguish different high scale SUSY-breaking structures, identify the scale at which the breaking is communicated to the visible sector, and determine the soft breaking parameters at that scale. In this work, we demonstrate that 1-loop Renormalization Group~(RG) invariant quantities present in the MSSM may be used to answer each of these questions. We apply our method first to generic flavor-blind models of SUSY-breaking, and then examine in detail the subset of these models described by General Gauge Mediation and the constrained MSSM with non-universal Higgs masses. As RG invariance generally does not hold beyond leading-log order, we investigate the magnitude and direction of the 2-loop corrections. We find that with superpartners at the TeV scale, these 2-loop effects are either negligible, or they are of the order of optimistic experimental uncertainties and have definite signs, which allows them to be easily accounted for in the overall uncertainty.Comment: v2 -- references added, version to be published in PRD; 40 page

New Approach to Parton Shower MC's for Precision QCD Theory: HERWIRI1.0(31)

By implementing the new IR-improved Dokshitzer-Gribov-Lipatov-Altarelli-Parisi-Callan-Symanzik (DGLAP-CS) kernels recently developed by one of us in the HERWIG6.5 environment we generate a new MC, HERWIRI1.0(31), for hadron-hadron scattering at high energies. We use MC data to illustrate the comparison between the parton shower generated by the standard DGLAP-CS kernels and that generated by the new IR-improved DGLAP-CS kernels. The interface to MC@NLO, MC@NLO/HERWIRI, is illustrated. Comparisons with FNAL data and some discussion of possible implications for LHC phenomenology are also presented.Comment: 24 pages, 10 figures; published versio