By Marvin V. Zelkowitz (Eds.)
This is often quantity seventy two of Advances in desktops, a sequence that started again in 1960 and is the oldest carrying on with sequence chronicling the ever-changing panorama of knowledge expertise. every year 3 volumes are produced, which current nearly 20 chapters that describe the most recent expertise within the use of pcs this 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 this day has turn into ubiquitous with hundreds of thousands of machines being offered (and discarded) each year. strong machines are produced for just a few hundred U.S. cash, and one of many difficulties confronted through 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 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 purposes that require huge strong machines. functions akin to climate and weather prediction, fluid move for designing new airplanes or autos, or nuclear plasma stream require as a lot computing device energy as we will supply, or even that's not adequate. brand new machines function on the teraflop point (trillions of floating element operations in keeping with moment) and this ebook describes study into the petaflop area (1,015 FLOPS). The six chapters offer an outline of present actions that may supply for the creation of those machines within the years 2011 via 2015
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Extra resources for COMPUTERSHigh Performance Computing
An alternative to performance counters is application tracing via code instrumentation. Tracing can, for example, discover memory addresses and locality, but is notoriously expensive . The methods we propose in this section determine the entries of A using three different methods, each with different trade-offs in accuracy versus effort. 2 Methods to Solve the Convolution Problem In this work, we investigate three methods for calculating A and determining the ⊗ operator. We classify the ﬁrst two methods as empirical and the third one as ab initio.
In other words, we looked for conditions to minimize: m,n predicted measured − Pi,j Pi,j Total error = measured Pi,j (5) i,j where m is the number of machines (5) and n is the number of loops (40). Finally, having developed the conditions for the training set that minimized total error, we used the same conditions to convolve and to predict the full AVUS and HYCOM applications, as well as a larger set of applications on a larger set of machines. 5 Experimental Results To evaluate the usefulness of the empirical and ab initio methods, we tested both on several strategic applications run at several processor counts and inputs on the systems listed in Table II.
Table IV Signed Error in Predicting the Performance of AVUS and CTH on Different Processor Counts on the ASC Altix, Using Application Signatures Generated Through Empirical Methods 10 Bin Error Application avus 32 avus 64 avus 128 cth 32 cth 64 cth 96 18 Bin LS LP LS LP 27% 18% 16% −108% −98% −171% 42% −27% 32% −64% −30% −170% −117% −135% −129% −56% −35% −47% −117% −100% −80% 8% −35% −44% machine other than the SDSC IA64 (already ﬂagged as suspect). In this case, we see that the predicted runtime is only rarely within even 20% of the actual runtime.