Instruction-Level Redundancy (ILR) is a well-known approach to tolerate transient CPU faults. It replicates instructions in a program and inserts periodic checks to detect and correct CPU faults using majority voting, which essentially requires three copies of each instruction and leads to high performance overheads. As SIMD technology can operate simultaneously on several copies of the data, it appears to be a good candidate for decreasing these overheads. To verify this hypothesis, we propose Elzar, a compiler framework that transforms unmodified multithreaded applications to support triple modular redundancy using Intel AVX extensions for vectorization. Our experience with several benchmark suites and real-world case-studies yields mixed results: while SIMD may be beneficial for some workloads, e.g., CPU-intensive ones with many floating-point operations, it exhibits higher overhead than ILR in many applications we tested. We study the sources of overheads and discuss possible improvements to Intel AVX that would lead to better performance.
@cite_16: Higher transistor counts, lower voltage levels, and reduced noise margin increase the susceptibility of multicore processors to transient faults. Redundant hardware modules can detect such errors, but software transient fault detection techniques are more appealing for their low cost and flexibility. Recent software proposals double register pressure or memory usage, or are too slow in the absence of hardware extensions, preventing widespread acceptance. This paper presents DAFT, a fast, safe, and memory efficient transient fault detection framework for commodity multicore systems. DAFT replicates computation across multiple cores and schedules fault detection off the critical path. Where possible, values are speculated to be correct and only communicated to the redundant thread at essential program points. DAFT is implemented in the LLVM compiler framework and evaluated using SPEC CPU2000 and SPEC CPU2006 benchmarks on a commodity multicore system. Results demonstrate DAFT's high performance and broad fault coverage. Speculation allows DAFT to reduce the perfor- mance overhead of software redundant multithreading from an average of 200 to 38 with no degradation of fault coverage.
@cite_7: Exponential growth in the number of on-chip transistors, coupled with reductions in voltage levels, makes each generation of microprocessors increasingly vulnerable to transient faults. In a multithreaded environment, we can detect these faults by running two copies of the same program as separate threads, feeding them identical inputs, and comparing their outputs, a technique we call Redundant Multithreading (RMT).This paper studies RMT techniques in the context of both single- and dual-processor simultaneous multithreaded (SMT) single-chip devices. Using a detailed, commercial-grade, SMT processor design we uncover subtle RMT implementation complexities, and find that RMT can be a more significant burden for single-processor devices than prior studies indicate. However, a novel application of RMT techniques in a dual-processor device, which we term chip-level redundant threading (CRT), shows higher performance than lockstepping the two cores, especially on multithreaded workloads.
Software-based hardening techniques can be broadly divided into three categories: Thread-Level Redundancy (TLR) also called Redundant Multithreading (RMT), Process-Level Redundancy (PLR), and Instruction-Level Redundancy ( ). Redundant Multithreading (RMT). In RMT approaches @cite_7 @cite_16 , a hardened program spawns an additional thread for each original thread. At runtime, trailing threads are executed on separate spare cores or take advantage of the Simultaneous Multithreading (SMT) capabilities of modern CPUs. Similar to , RMT allows keeping only one memory state among replicas (assuming that memory is protected via ECC). However, RMT approaches heavily rely on the assumption of spare cores or unused SMT, which is commonly not the case in multithreaded environments where programs tend to use all available CPU cores.