Temperature-Aware Leakage Minimization Techniques for Real-Time Systems

In this paper, we study the interdependencies between system's leakage and on-chip temperature. We show that the temperature variation caused by on-chip heat accumulation has a large impact in estimating the system's leakage energy. More importantly, we propose an online temperature-aware leakage minimization technique to demonstrate how to incorporate the temperature information to reduce energy consumption at real time. The basic idea is to run when the system is cool and the workload is high and to put the system to sleep when it is hot and the workload is light. The online algorithm has low run-time complexity and achieves significant leakage energy saving. In fact, we are able to get about 25% leakage reduction on both real life and artificial benchmarks. Comparing to our optimal offline algorithm, the above online algorithm provides similar energy savings with similar decisions on how to put the system to sleep and how to wake it up. Finally, our temperature-aware leakage minimization techniques can be combined with existing DVS methods to improve the total energy efficiency by further saving on leakage

Speculative Data Distribution in Shared Memory Multiprocessors

This work explores the possibility of using speculation at the directories in a cache coherent non-uniform memory access multiprocessor architecture to improve performance by forwarding data to their destinations before requests are sent. It improves on previous consumer prediction techniques, showing how to construct a predictor that can handle a tradeoff of accuracy and coverage. This dissertation then explores the correct time to perform consumer prediction, and show how a directory protocol can incorporate such a scheme. The consumer prediction enhanced protocol that is developed is able to reduce the runtime of a set of scientific benchmarks by 10%-20%, without substantially reducing the runtime of other benchmarks; specifically, those benchmarks feature simple phased behavior and regularly distribute data to more than two processors. This work then explores the interaction of consumer prediction with two other forms of prediction, migratory prediction and last touch prediction. It demonstrates a mechanism by which migratory prediction can be implemented using only the storage elements already present in a consumer predictor. By combining this migratory predictor with a consumer predictor, it is possible to produce greater speedups than did either individually. Finally, the signatures of the last touch predictor can be applied to improve the performance of consumer prediction

Individual common variants exert weak effects on the risk for autism spectrum disorders.

While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest

A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data

Perceptron based consumer prediction in shared-memory multiprocessors

Abstract — Recent research has shown that forwarding speculative data to other processors before it is requested can improve the performance of multiprocessor systems. The most recent work in speculative data forwarding places all of the processors on a single bus, allowing the data to be forwarded to all of the processors at the same cost as any subset of the processors. Modern multiprocessors however often employ more complex switching networks in which broadcast is expensive. Accurately predicting the consumers of data can be challenging, especially in the case of programs with many shared data structures. Past consumer predictors rely on simple prediction mechanisms, a single table lookup followed by a static mapping of the table values onto a prediction. We make two main contributions in this paper. First, we show how to reduce the design space of consumer predictors to a set of interesting predictors, and how previous consumer predictors can be tuned to expand the range of available performance. Second, we propose a perceptron consumer predictor that dynamically adapts its reaction to the system behavior, and uses more history information than previous consumer predictors. This predictor outperforms the previous predictors by 21 % while using only 1KByte more storage than previous predictors. I