DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Improving redundant multithreading performance for soft-error detection in HPC applications

Tesis de Graduación (Maestría en Computación) Instituto Tecnológico de Costa Rica, Escuela de Computación, 2018As HPC systems move towards extreme scale, soft errors leading to silent data corruptions become a major concern. In this thesis, we propose a set of three optimizations to the classical Redundant Multithreading (RMT) approach to allow faster soft error detection. First, we leverage the use of Simultaneous Multithreading (SMT) to collocate sibling replicated threads on the same physical core to efficiently exchange data to expose errors. Some HPC applications cannot fully exploit SMT for performance improvement and instead, we propose to use these additional resources for fault tolerance. Second, we present variable aggregation to group several values together and use this merged value to speed up detection of soft errors. Third, we introduce selective checking to decrease the number of checked values to a minimum. The last two techniques reduce the overall performance overhead by relaxing the soft error detection scope. Our experimental evaluation, executed on recent multicore processors with representative HPC benchmarks, proves that the use of SMT for fault tolerance can enhance RMT performance. It also shows that, at constant computing power budget, with optimizations applied, the overhead of the technique can be significantly lower than the classical RMT replicated execution. Furthermore, these results show that RMT can be a viable solution for soft-error detection at extreme scale

ROSE::FTTransform - A source-to-source translation framework for exascale fault-tolerance research

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])

Evaluating Software-based Hardening Techniques for General-Purpose Registers on a GPGPU

Graphics Processing Units (GPUs) are considered a promising solution for high-performance safety-critical applications, such as self-driving cars. In this application domain, the use of fault tolerance techniques is mandatory to detect or correct faults, since they must work properly even in the presence of faults. GPUs are designed with aggressive technology scaling, which makes them susceptible to faults caused by radiation interference, such as the Single Event Upsets (SEUs), which can lead the system to fail, and that is unacceptable in safety-critical applications. In this paper, we evaluate different software-based hardening techniques developed to detect SEUs in GPUs general-purpose registers and propose optimizations to improve performance and memory utilization. The techniques are implemented in three case-study applications and evaluated in a general-purpose soft-core GPU based on the NVIDIA G80 architecture. A fault injection campaign is performed at register transfer level to assess the fault detection potential of the implemented techniques. Results show that the proposed improvements can be tailored for different scenarios, helping engineers in navigating the design space of hardened GPGPU applications