Parallelizing genetic linkage analysis: a case study for applying parallel computation in molecular biology.
Parallel computers offer a solution to improve the lengthy computation time of many conventional, sequential programs used in molecular biology. On a parallel computer, different pieces of the computation are performed simultaneously on different processors. LINKMAP is a sequential program widely used by scientists to perform genetic linkage analysis. We have converted LINKMAP to run on a parallel computer, using the machine-independent parallel programming language, Linda. Using the parallelization of LINKMAP as a case study, the paper outlines an approach to converting existing highly iterative programs to a parallel form. The paper describes the steps involved in converting the sequential program to a parallel program. It presents performance benchmarks comparing the sequential version of LINKMAP with the parallel version running on different parallel machines. The paper also discusses alternative approaches to the problem of "load balancing," making sure the computational load is shared as evenly as possible among the available processors.
Miller
P L
PL
Yale University School of Medicine, New Haven, Connecticut 06510.
Nadkarni
P
P
Gelernter
J E
JE
Carriero
N
N
Pakstis
A J
AJ
Kidd
K K
KK
D002874:Chromosome Mapping; D005544:Forecasting; D008040:Genetic Linkage / Q000235:genetics*; D008126:Lod Score; D008967:Molecular Biology / Q000379:methods*; D011381:Programming Languages; D012984:Software; D012985:Software Design