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COMPUTERSHigh Performance Computing by Marvin V. Zelkowitz (Eds.)

By Marvin V. Zelkowitz (Eds.)

This is often quantity seventy two of Advances in desktops, a sequence that all started again in 1960 and is the oldest carrying on with sequence chronicling the ever-changing panorama of data expertise. every year 3 volumes are produced, which current nearly 20 chapters that describe the newest expertise within the use of pcs this present day. during this quantity seventy two, we current the present prestige within the improvement of a brand new iteration of high-performance desktops.

The desktop at the present time has turn into ubiquitous with hundreds of thousands of machines being offered (and discarded) every year. strong machines are produced for just a few hundred U.S. money, and one of many difficulties confronted via owners of those machines is that, as a result carrying on with adherence to Moore's legislation, the place the rate of such machines doubles approximately each 18 months, we regularly have good enough machine energy for our wishes for notice processing, browsing the net, or enjoying games. even though, an analogous can't be stated for functions that require huge strong machines. purposes akin to climate and weather prediction, fluid stream for designing new airplanes or autos, or nuclear plasma move require as a lot laptop strength as we will offer, or even that's not sufficient. cutting-edge machines function on the teraflop point (trillions of floating element operations in step with moment) and this publication describes learn into the petaflop sector (1,015 FLOPS). The six chapters supply an outline of present actions that might offer for the creation of those machines within the years 2011 via 2015

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Each method requires different amounts of initial data and user input, and each reveals different information. We described two empirical methods which assume that some runtimes are known and used them to determine application characteristics in different ways. The least squares method determines how well the existing data can be explained, given that particular assumptions are made. The linear programming method can additionally correctly identify systems with erroneous benchmarking data (assuming that the architectures of the target systems are roughly similar).

Homogenous multi-core systems require coarse-grain, multi-threaded implementations, while GPUs and Cell systems require users to manage parallelism and orchestrate data movement explicitly. Taken together, these attributes form the greatest challenge for these architectures in terms of developer productivity, code portability and performance. In this project, we have characterized the relative performance improvements and required programming effort for two diverse workloads across these architectures: contemporary multi-core processors, Cell, GPU, and MTA-2.

I51 Serial Code . i52 Subproblem B i51 43 i50 Concurrent threads of computation Subproblem A .... Hardware streams (128) Unused streams Instruction Ready Pool; Pipeline of executing instructions Fig. 9. Block diagram of the MTA-2 system. 2 Target Workloads We target two algorithms for our initial evaluations: an imaging application and a floating-point-intensive scientific algorithm. 1 Hyperspectral Imaging (HSI) A covariance matrix is created in a number of imaging applications, such as hyperspectral imaging (HSI).

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