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Given a chip that produced a faulty output response to a test set, a defect diagnosis procedure produces a set of candidate faults that is expected to identify the defects that are present in the chip. The accuracy of the set of candidate faults is higher when the set is smaller or when its overlap with the defects that are present in the chip is larger. To increase the accuracy of a set of candidate faults, this paper describes an approach where several sets of candidate faults are computed based on different subsets of the test set. The subsets are obtained by removing small numbers of tests from the complete test set. The result is sets of candidate faults that are similar but not identical. The number of sets where a fault appears yields a confidence level that the fault actually belongs in a set of candidate faults. New sets of candidate faults are defined based on the confidence levels obtained. The smallest set of candidate faults can be used as the final result of defect diagnosis, or the sets can be used for ranking the candidates. Experimental results for benchmark circuits demonstrate the effectiveness of this approach. [ABSTRACT FROM PUBLISHER]

Scratchpad memories in GPU architectures are employed as software-controlled caches to increase the effective GPU memory bandwidth. Through the use of well-known optimization techniques, such as privatization and tiling, they are properly exploited. Typically, they are banked memories which are addressed with a \textmod(2^N) bank indexing scheme. Although their bandwidth is fully exploited for linear memory accesses, their performance is burdened when non-unit strides appear in memory access patterns because they provoke bank conflicts. This paper explores the use of configurable bit-vector and bitwise XOR-based hash functions to evenly distribute memory addresses of the access patterns over the memory banks, reducing the number of bank conflicts. An exhaustive, but lightweight, search is used to configure bit-vector hash functions. Bitwise hash functions are configured with heuristics. Hardware and software implementations are carried out. For the hardware approach, the experimental results show 24 percent performance speed-up for 22 benchmarks on GPGPU-Sim, a Fermi-like simulator. Bank conflicts are reduced by 96 percent with bit-vector hash functions, and 97 percent with bitwise hash functions using our proposed Minimum Imbalance Heuristic. The software approach, using bit-vector hash functions, demonstrates 23 percent speed-up and 96 percent bank conflict reduction on a Fermi GPU, and 33 percent speed-up and 99 percent bank conflict reduction on a Kepler GPU. [ABSTRACT FROM AUTHOR]

Software Transactional Memory (STM) has recently emerged as a promising synchronization abstraction for multicore architectures. State-of-the-art STM algorithms, however, suffer from performance challenges due to contention and spinning on locks during the transaction commit phase. In this paper, we introduce Remote Transaction Commit (or RTC), a mechanism for executing commit phases of STM transactions. RTC dedicates server cores to execute transactional commit phases on behalf of application threads. This approach has two major benefits. First, it decreases the overheads of spinning on locks during commit, such as the number of cache misses, blocking of lock holders, and CAS operations. Second, it enables exploiting the benefits of coarse-grained locking algorithms (simple and fast lock acquisition, reduced false conflicts) and bloom filter-based algorithms (concurrent execution of independent transactions). Our experimental study on a 64-core machine with four sockets shows that RTC solves the problem of performance degradation due to spin locking on both micro-benchmarks (red-black trees), and macro-benchmarks (STAMP), especially when the commit phase is relatively long and when thread contention increases. [ABSTRACT FROM PUBLISHER]