Akihiro Nakaya, K. Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Compt. Appl. Biosci., Vol. 11, pp. 685--692, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....= 1; k 2 f1; 5; 10g is shown as the representative result . Richards: The Richards benchmark is an operating system simulation that is used as a nontrivial program in evaluating several object oriented languages[14] RNA: RNA is a parallel search program for predicting RNA secondary structures[91, 119]. This program uses an object to share information on the best answers that have been discovered among concurrently running threads. Each thread in the system constantly checks the object, and terminates (i.e. prunes) itself when there are no chances to find a better answer than the best answers ....

Akihiro Nakaya, K. Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Compt. Appl. Biosci., Vol. 11, pp. 685--692, 1995.

.... Ipsc based hardware, such as the Hypercube and the Delta (Hofacker et al. 1994, 1996a) Zuker s suboptimal folding algorithm (Zuker, 1989) was ported to a MasPar MP 2 (Shapiro et al. 1995) and an approximate folding procedure that also yields suboptimal structures has been described for a CM 5 (Nakaya et al. 1995). In this contribution we discuss an implementation of McCaskill s partition function folding algorithm for message passing architectures. Our program, which is available upon request, is written in C and uses the MPI message passing interface. It should therefore be easily portable to most ....

Nakaya, A., Yamamoto, K., and Yonezawa, A., 1995. RNA secondary structure prediction using highly parallel computers. Comput. Appl. Biosci. 11, 685--692.

....This is a satisfying procedure that gives excellent results, including pseudo knots identification. However the procedure requires a prior alignment of sequences and multiple alignment is, in turn, a difficult problem. One must also quote miscellaneous methods based either on parallel algorithms (Nakaya et al. 1995 ; Shapiro and Wu, 1997) or formal grammar (Sakakibara et al. 1994 ; Grate, 1995 ; Lefebvre, 1995 ; Lefebvre, 1996) graph theory (Cary and Stormo 1995) and simulation of the RNA folding process (Martinez 1983 ; Martinez 1988 ; Wuju and JiaJin, 1998) Recent methods intend both to align RNA ....

Nakaya, A., Yamamoto, K. and Yonesawa, A. (1995) RNA secondary structure prediction using highly parallel computers, Comput. Applic. Biosci., 11, 685-692.

....and Younger [43] A survey of parallel algorithms is given in [50] Refer to [54] for our algorithm. RNA is a parallel tree search program that predicts the secondary structure of an RNA molecule from a given sequence of bases. The original program was written by Nakaya in C using message passing [49] as well as a concurrent object oriented language Schematic [72] A simplified version is written in ABCL f by the author and used in the evaluation in this thesis. GA is a parallel genetic algorithm written by Hiyane [35] 1.5 Roadmap The rest of the thesis is organized as follows. Chapter 2 ....

Akihiro Nakaya, Kenji Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Comput. Applic. Biosci. (CABIOS), 11, 1995.

....simple core of the language and the conciseness convenience in typical programs. A prototype on top of a sequential Scheme (Scheme C) has been implemented and is running on AP1000 and AP1000 massively parallel processors [15, 28] We had developed an RNA secondary structure prediction algorithm [22], 7 which is essentially a parallel tree search with application specific priority and a load balancing control scheme, and Barnes Hut Nbody algorithm. Experiments on an AP1000 system (SuperSparc 50 Mhz 2 256) indicated an usable performance, though many more improvements are necessary. ....

Akihiro Nakaya, Kenji Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Comput. Applic. Biosci. (CABIOS) (to appear), 11, 1995.

....repeatedly calling a method that immediately performs become. Richards: It is an operating system simulation that is used as a nontrivial program for evaluating performance of several object oriented languages [CHA 89] RNA is a parallel search program for predicting RNA secondary structures [NAK 95] This program uses an object to maintain and to share information the found answers among concurrently running threads. Since Richards and RNA use both functions and methods, their executions show how the efficiency of the meta objects affects overall execution speed in realistic applications. ....

NAKAYA, A., YAMAMOTO, K., and YONEZAWA, A. RNA secondary structure prediction using highly parallel computers. Compt. Appl. Biosci., Vol. 11, p. 685--692, 1995.

....was proposed by Cocke, Kasami, and Younger [20, 34] A survey of parallel algorithms is given in [24] CKY is a heap intensive application. Communication is frequent and synchronous. The amount of intra node parallelism depends on the number of processors as well as on the input sentence. RNA [23] is a parallel tree search program which predicts the secondary structure of an RNA molecule from a given sequence of bases. RNA is an asynchronous 6 Semantically, a local procedure method call also creates a reply channel. The runtime system optimizes them in most cases so that no heap ....

Akihiro Nakaya, Kenji Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Comput. Applic. Biosci. (CABIOS), 11, 1995.

.... data structure (called BH tree) Particles are partitioned into processors and each processor calculates acceleration of Application Description FK SW fork a thread at: SW switches a thread when: BH [3] Barnes Hut Nbody each visit at BH tree node the node is not accessed recently simulation RNA [19] Parallel tree each visit to a node in the search tree the depth of the node D (a constant) search CKY [23] CFG parser by each computation of S i;j S i;j accesses S i;k or Sk;j before CKY algorithm computed Table 4: Benchmark Applications Application T SQ T FK T SW # of forks (F ) # of ....

....and the number of forks, potentially blocking points, and actual blockings (F , S, and B, respectively) the assigned particles sequentially. We only measured the force calculation phase of the algorithm. RNA: Parallel RNA secondary structure prediction by combinatorial search pruning [19]. Parallelism is simply extracted by parallel recursion until the depth of the recursive call reaches a given limit. The limit is given by the user at runtime. The appropriate value for the limit depends on input data and pruning conditions, thus is not predictable at compile time. CKY: CFG ....

Akihiro Nakaya, Kenji Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Comput. Applic. Biosci. (CABIOS), 11, 1995.

....simple core of the language and the conciseness convenience in typical programs. A prototype on top of a sequential Scheme (Scheme C) has been implemented and is running on AP1000 and AP1000 massively parallel processors [15, 28] We had developed an RNA secondary structure prediction algorithm [22], 8 which is essentially a parallel tree search with application specific priority and a load balancing control scheme, and Barnes Hut Nbody algorithm. Experiments on an AP1000 system (SuperSparc 50 Mhz 2 256) indicated an usable performance, though many more improvements are necessary. Further ....

Akihiro Nakaya, Kenji Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Comput. Applic. Biosci. (CABIOS) (to appear), 11, 1995.

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Akihiro Nakaya, Kenji Yamamoto, and Akinori Yonezawa. RNA secondary structure prediction using highly parallel computers. Comput. Appl. Biosci. 11, 685--692 (1995).

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