سنقدم في هذا البحث استراتيجية ذاتية التكيف تمكننا من كتابة خوارزمية متوازية تتكيف
مع عدد الموارد المتوفرة على البيئة التفرعية المخصصة لتنفيذ البرنامج المتوازي. إن
التطبيقات المتوازية المدروسة و المعنية بالبحث هي تطبيقات ممثلة بمخطط تدفق البيانات
المبني ديناميكياً خلال التنفيذ. تقوم الطريقة المقترحة هنا على المزاوجة بين خوارزمية
تسلسلية و أخرى متوازية معتمدين على مبدأ سرقة العمل في جدولة المهام. و نقدم دراسة
لتعقيد هذه الخوارزمية المتكيفة و تحليل لأدائها على معالج و مقارنته مع خوارزمية
تفرعية تقليدية.
We introduce an auto adaptive strategy enables to write a parallel
algorithm adapts to the number of available resources at allocated
parallel environment to execute the parallel program. The parallel
applications we are studying which are represented by data-flow
graph which built dynamically during the execution. The new
suggested strategy is based on coupling of a sequential algorithm
and a parallel one and relies on the principle of work stealing in
the tasks scheduling. We offer a study of the complexity of the
adaptive algorithm and analyze its performance on processors
and compare it with a performance of a classic parallel algorithm.
References used
BENDER MA, RABIN MO, 2002, Online scheduling of parallel programs on heterogeneous systems with applications to cilk, Theory Comput Syst. Vol 35.3, pp 289-304
BERNARD J, TRAORE D, ROCH JL, 2006, On-line adaptive parallel prefix computation, Euro-Par, vol 4128, pp 841-850
GALILEE F, CAVALHEIRO G, DOREILLE M, ROCH JL, 1998, Athapascan-1: On-Line Building Data Flow Graph in a Parallel Language, International Conference on Parallel Architectures and Compilation Techniques, PACT'98, pp 88–95
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