Predicting the Performance of MPI Applications over Different Grid Architectures

في الوقت الحاضر خوارزميات التحسين عالية السرعة تكون مطلوبة. في معظم الحالات ، يحتاج الباحثــــــــون إلى طريقة للتنبؤ ببعض المعايير بدقة مقبولة لاستخدامها في خوارزمياتهم. ومع ذلك ، في مجال الحوسبة المتوازية يمكن اعتبار وقت التنفيذ من أهم المعايير. لذا، يعرض هذا البحث نموذجًا جديدًا للتنبؤ بالوقت للتنفيذ لتطبيقات المتوازيـــــة الموزعة المنفذه على العديد من سيناريوهات الشبكة. حيث يمتلك النموذج المقترح القدرة علـــــــــى التنبؤ بوقت تنفيذ التطبيقات المتوازية التي تعمل عبر أي تكوين للشبكة من حيث عدد العقد المختلفة وقوى الحوسبة الخاصة بها. لقد تم تنفيذ التجارب على المحاكي  سمكرد الذي يمتلك خاصية السهولة في بناء نماذج شبكية متعدد ومختلفة. نتائــج الاختبارات بين اوقات التنفيذ الاصلية والاوقات المتنبئة بينت دقة تجيربية جيده. معدل الخطأ النسبي بين وقت التنفيذ الاصلي والمتنبأ لثلاث برامج معيارية تكون هي 4.36٪، 5.79٪ و 6.81٪.Nowadays, the high speed and accurate optimization algorithms are required. In most of the cases, researchers need a method to predict some criteria with acceptable accuracy to use it after in their algorithms. However, in the field of parallel computing the execution time can be considered the most important criteria. Consequently, this paper presents new execution time prediction model for message passing interface applications execute over numerous grid scenarios. The model has ability to predict the execution time of the message passing applications running over any grid configuration in term of different number of nodes and their computing powers. The experiments are evaluated over SimGrid simulator to simulate the grid configuration scenarios. The results of comparing the real and the predicted execution time show a good accuracy. The average error ratio between the real and the predicted execution time for three benchmarks are 4.36%, 5.79% and 6.81%

Multi-objective Optimization of Grid Computing for Performance, Energy and Cost

In this paper, new multi-objective optimization algorithm is proposed. It optimizes the execution time, the energy consumption and the cost of booked nodes in the grid architecture at the same time. The proposed algorithm selects the best frequencies depends on a new optimization function that optimized these three objectives, while giving equivalent trade-off for each one. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption of the message passing parallel iterative method executed over grid. DVFS is also reduced the computing power of each processor executing the parallel applications. Therefore, the performance of these applications is decreased and so on the payed cost for the booking nodes is increased.  However, the proposed multi-objective algorithm gives the minimum energy consumption and minimum cost with maximum performance at the same time. The proposed algorithm is evaluated on the SimGrid/SMPI simulator while running the parallel iterative Jacobi method. The experiments show that it reduces on average the energy consumption by up to 19.7 %, while limiting the performance and cost degradations to 3.2 % and 5.2 % respectively