Abstract: Though massively parallel SIMD arrays continue to be promising for many computer vision applications, they have undergone few systematic empirical studies. The problems include the size of the architecture space, the lack of portability of the test programs, and the inherent complexity of simulating up to hundreds of thousands of processing elements. The latter two issues have been addressed previously, here we describe how spreadsheets and tk/tcl are used to endow our simulator with the flexibility to model a large variety of designs. The utility of this approach is shown in the second half of the paper where results are presented as to the performance of a large number of array size, datapath, register file, and application code combinations. The conclusions derived include the utility of multiplier and floating point support, the cost of virtual PE emulation, likely datapath/memory combinations, and overall designs with the most promising performance/chip area ratios.

While the use of MapReduce systems (such as Hadoop) for large scale data analysis has been widely recognized and studied, we have recently seen an explosion in the number of systems developed for cloud data serving. These newer systems address “cloud OLTP ” applications, though they typically do

regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems

Simultaneous multithreading (SMT) is an architectural technique in which the processor issues multiple instructions from multiple threads each cycle. While SMT has been shown to be effective on scientific workloads, its performance on database systems is still an open question. In particular

Reduce benchmarks are ill-equipped to supply. In this paper, we build the case for going beyond benchmarks for MapReduce performance evaluations. We analyze and compare two production MapReduce traces to develop a vocabulary for describing MapReduce workloads. We show that existing benchmarks fail to capture rich

on an enhanced digital map, and/or on sensors for physiological and behavioral workload correlates. In this report, we address first results of our approach towards the following questions: • According to our experiments, what method is best for online/predictive workload estimation? • Which sensors are most

Disk subsystem performance can be dramatically improved by dynamically ordering, or scheduling, pending requests. Via strongly validated simulation, we examine the impact of complex logical-to-physical mappings and large prefetching caches on scheduling effectiveness. Using both synthetic workloads

DAG mapping algorithm is presented for multicluster architectures. Each constituent cluster in the multicluster is shared by background workload (from other users) and has its own independent local scheduler. The multicluster DAG mapping policy is based on theoretical analysis and its performance

MapReduce workloads have evolved to include increasing amounts of time-sensitive, interactive data analysis; we refer to such workloads as MapReduce with Interactive Analysis (MIA). Such workloads run on large clusters, whose size and cost make energy efficiency a critical concern. Prior works

outcomes. It describes a black-box approach for testing for these properties, and uses the approach to characterize the semantics of 23 non-trivial MapReduce workloads. Surprisingly, we found that for most requirements, there is at least one workload that violates it. This means that MapReduce may