Download High performance computing by Marvin Zelkowitz Ph.D. MS BS. PDF

By Marvin Zelkowitz Ph.D. MS BS.

This can be quantity seventy two of Advances in pcs, a sequence that begun again in 1960 and is the oldest carrying on with sequence chronicling the ever-changing panorama of knowledge know-how. 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 pcs. the pc this day has turn into ubiquitous with hundreds of thousands of machines being bought (and discarded) each year. strong machines are produced for just a few hundred U.S. cash, and one of many difficulties confronted through proprietors of those machines is that, because of the carrying on with adherence to Moore's legislations, the place the rate of such machines doubles approximately each 18 months, we normally have good enough computing device energy for our wishes for observe processing, browsing the internet, or enjoying games. even though, an analogous can't be acknowledged for functions that require huge strong machines. functions comparable to climate and weather prediction, fluid circulate for designing new airplanes or cars, or nuclear plasma movement require as a lot computing device strength as we will offer, or even that isn't sufficient. cutting-edge machines function on the teraflop point (trillions of floating aspect operations in keeping with moment) and this e-book describes study into the petaflop sector (1,015 FLOPS). The six chapters offer an outline of present actions that might supply for the creation of those machines within the years 2011 via 2015.

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P1,n m1,1 . . m1,c ⎤ ⎡ ⎢p2,1 . . p2,n ⎥ ⎢m2,1 . . m2,c ⎥ a1,1 . . a1,n ⎢ ⎥ ⎢ ⎥ ⎢p3,1 . . p3,n ⎥ ⎢m3,1 . . m3,c ⎥ ⎢ .. ⎥ .. (3) ⎢ ⎥=⎢ ⎥⊗⎣ . . ⎦ ⎢ .. ⎥ ⎢ ⎥ . . .. ⎦ ⎣ .. ⎦ ⎣ . . ac,1 . . ac,n pk,1 . . pk,n mk,1 . . mk,c Given Equation 3 as the general convolution problem, the relevant questions are how to determine the entries of M and A, and what to use for the ⊗ operator to generate accurate performance predictions in P? Populating M is fairly straightforward, at least if the machine or a smaller prototype for the machine exists.

These problems can be solved by a technique described in [9] and implemented as lsqnonneg in Matlab. In practice, before applying the non-negative least squares solver, we normalize both the rows and columns of the equation with respect to the largest entry in each column. , network latency versus time to access the L1 cache). Rescaling of the rows of M and Pi so that the entries of P are all 1 allows us to normalize for different runtimes. The least squares approach has the advantage of being completely automatic, as there are no parameters that need to be changed or no constraints that may need to be discarded.

Similar expansion could be done for Rate, making it a k × c matrix, where k is the total number of machines, each of which is characterized by c operation rates. This would make P a k × n matrix in which Pij is the predicted runtime of application i on machine j. That is, the generalized Equation 2 represents the calculation of predicted runtimes of n different applications on k different machines and can be expressed as P = Rate ⊗ OpCount. Since each column in OpCount can also be viewed as the application signature of a specific application, we refer to OpCount as the application signature matrix, A.

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