Almasi94 George S. Almasi, Allan Gottlieb, Highly Parallel Computing, Second Edition, The Benjamin/Cummings Publishing Company, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....since 1996. We began research on parallelism in 1989, with Transputer based parallel computers[4] A Transputer was a processor specially designed for parallelism, and Transputer networks could be programmed in Occam or C using a message passing library based on CSP model (rendez vous mechanism) [1]. Later we used some other distributed memory architectures[8] Intel Paragon and Cray T3D, that could be programmed using specific or standard parallel paradigms. More recently we have used some shared memory parallel computers [2] SGI Power Challenge Array and SGI Origin2000, supporting various ....

G. Almasi and A. Gottlieb. Highly parallel computing. Benjamin/Cummings publishing company, 1994. second edition.

....at higher problem sizes. The figure also shows the routines that have been parallelized. As can be seen from the plot, about 60 to 70 of the sequential code is parallelized. Consequently, the maximum theoretical speedup that can be achieved from this computation as given by Amdahl s Law [1] is approximately 3. Our BSP implementation achieves a maximum speedup of about 2.5, which is obtained with 3 processors. 42 Figure 3.5: A sample graph with 12 vertices. Degree of each vertex is shown in parentheses 3.6 Finding the Maximum Independent Set of a Graph A set of vertices in a graph ....

George S. Almasi and Allan Gottlieb. Highly Parallel Computing. The Benjamin/Cummings Publishing Company, Inc., Redwood City, CA, 1994.

....basic configuration used to link workstations into a cluster suing HSSI switch. This switching node can be cascaded into a larger switch network as shown in Figure 8. Figure 7 Basic configuration for Workstation Cluster using HSSI switch When we cascade the HSSI switch into a multistage network[8,9,10], the first stage switches will determine the of routing bits position from the destination address field using a preset hard switches inside each node (e.g. bit 1 and bit 2) After the position is known, this routing bit is used to determine a routing path in this stage, This process is repeated ....

Alamsi G.S. and Gottlieb A., Highly Parallel Computing, The Benjamin/Cummings Publishing Company, Redwood City, 1994.

....and seek to support more general classes of ANNs [8] 15] 21] Although some neurocomputers could potentially support dynamic topologies more directly in hardware, rather than in software, they currently do not. Of course, general parallel machines, like the Connection Machine [9] and the CRAY [1], can simulate the desired dynamics in software, but these machines are not optimized for neural computation. LIT supports general classes of ANNs and dynamic topologies in an efficient parallel hardware implementation. LIT redesigns the original network into a hierarchical, parallel network of ....

G. S. Almasi, A. Gottlieb, Highly Parallel Computing, The Benjamin/Cummings Publishing Company, Inc, Redwood City, CA, 1989.

....and seek to support more general classes of ANNs [8] 18] 23] Although some neurocomputers could potentially support dynamic topologies more directly in hardware, rather than in software, they currently do not. Of course, general parallel machines, like the Connection Machine [9] and the CRAY [1], can simulate the desired dynamics in software, but these machines are not optimized for neural computation. LIT supports general classes of ANNs and dynamic topologies in an efficient parallel hardware implementation. LIT maps the original network into a hierarchical, parallel network of ....

G. S. Almasi, A. Gottlieb, Highly Parallel Computing, The Benjamin/Cummings Publishing Company, Inc, Redwood City, CA, 1989.

....according to the number of processors, such as t ksimul = t simul = e:P ) with 0 e 1 is the eciency of the parallelization, and P is the number of processors. Table 1 represents the speed up and the parallelization eciency over the SGI Origin 2000 too. The speed up is S(P ) T (1) T (P ) [1], which is equal to the ratio of the division of the execution time over 1 processor per that over P processors. The eciency is e(P ) S(P ) S ideal (P ) S(P ) P [1] since the ideal speed up that we can obtain with P processors is equal to P . The eciency is then the fraction of the ideal ....

....Table 1 represents the speed up and the parallelization eciency over the SGI Origin 2000 too. The speed up is S(P ) T (1) T (P ) 1] which is equal to the ratio of the division of the execution time over 1 processor per that over P processors. The eciency is e(P ) S(P ) S ideal (P ) S(P ) P [1], since the ideal speed up that we can obtain with P processors is equal to P . The eciency is then the fraction of the ideal speed up that we can obtain with a certain parallelization. We notice that if the execution time decreases, the speed up increases slowly and the eciency falls quickly. ....

G. S. Almasi and A. Gottlieb. Highly parallel computing. The Benjamin/Cummings publishing company, Inc, 1989.

....the Scoreboard [50, 20] and the Tomasulo algorithm [51, 20] The Pentium, for example, applies a Scoreboard mechanism, whereas PowerPC, Pentium Pro and AMD K10 apply the Tomasulo algorithm. 2 COMPUTER ARCHITECTURE 6 Correctness of the Schedulers In the standard literature on computer architecture [20, 1, 30, 28] the correctness of a scheduling mechanism is usually motivated by the fact that the structural and control hazards, as well as the standard data hazards (write after write, read after write, write after read) are properly resolved. However, this hazard criterion is not sufficient for the ....

G.S. Almasi and A. Gottlieb. Highly Parallel Computing. The Benjamin Cummings Publishing Company, Inc., 1994.

....parallelism and an appropriate evaluation order of functions. So better program performance can be achieved through less idle time and less communication amount. A prominent example is SISAL, whose optimized compiler made the weather code run six times faster than the Fortran implementation did [1]. 3 Function Definitions for Data Flow Graphs A FASAN program consists in a set of function definitions. A function represents (part of) a data flow graph. Every function called in the definition body is represented by a graph node. The edges of the graph are the input, local, and result ....

G. S. Almasi and A. Gottlieb. Highly Parallel Computing. Benjamin Cummings Publishing Company, Redwood City (CA) et al., 1994.

....After cycle (i) instruction I i has given free all the resources involved in its execution. During a cycle t, an instruction I i is called active, iff it got issued before t and did not terminate yet, i.e. i) t (i) 2.3. Data Hazards In the standard literature on computer architecture [4, 1, 6] the correctness of a scheduling mechanism is usually motivated by the fact that the following three types of data hazards (dependencies) are properly resolved and do not cause read write conflicts: Definition 2.2 (Write After Write) In a program P , there is a WAW dependency WAW(i,j) between two ....

G. Almasi and A. Gottlieb. Highly Parallel Computing. The Benjamin Cummings Publishing Company, Inc., 1994.

....to a specific computer, the library programmer needs to have a profound knowledge of the following aspects of a computer: ffl (i) The architecture. Modern SMMs offer at least two levels of parallelism. At a coarse level, the exploitation of the parallelism offered by the different processors [AlGo89] and, at a finer level, the exploitation of the program s Instruction Level Parallelism (ILP) by modern superscalar processors [HePa96] ffl (ii) The software tools offered by SMMs. Those can be viewed at three different levels. First, one can tell the compiler where to find coarse grain ....

G. Almasi, A. Gottlieb, Highly Parallel Computing, Benjamin Cummings Publishing Company, Inc. 1989.

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G. S. Almasi and A. Gottlieb, Highly Parallel Computing, The Benjamin/Cummings Publishing Company, Inc., 1989.

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Almasi94 George S. Almasi, Allan Gottlieb, Highly Parallel Computing, Second Edition, The Benjamin/Cummings Publishing Company, 1994.

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George S. Almasi and Allan Gottlieb. Highly Parallel Computing.The Benjamin/Cummings Publishing Company, Inc., 1989.

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Almasi, G.S. and Gottlieb, A. Highly Parallel Computing. (2nd ed.). Benjamin /Cummings Publishing Company, 1994.

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G.S. Almasi and A. Gottlieb. Highly parallel computing, chapter 1. The Benjamin /Cummings publishing company, second edition, 1994.

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G. S. Almasi and A. Gottlieb. Highly Parallel Computing. The Benjamins/Cummings Publishing Company, Redwood City, CA, 1989.

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G. S. Almasi and A. Gottlieb, Highly Parallel Computing (The Benjamin Cummings Publishing Company, Inc., 1994).

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G. S. Almasi and A. Gottlieb. Highly Parallel Computing. The Benjamin-Cummings Publishing Company, Inc., 1989.

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