ارسل ملاحظاتك

ارسل ملاحظاتك لنا







يجب تسجيل الدخول أولا

Performance Modeling And Analysis Of Parallel Gaussian Elimination On Multi-core Computers

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Sibai, Fadi N. (Author)
المجلد/العدد: مج26, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2014
الصفحات: 41 - 54
DOI: 10.33948/0584-026-001-005
ISSN: 1319-1578
رقم MD: 973012
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Gaussian Elimination | Multi-core Computing | Performance Modeling And Analysis
رابط المحتوى:
صورة الغلاف QR قانون
حفظ في:
LEADER 02510nam a22002297a 4500
001 1715916
024 |3 10.33948/0584-026-001-005 
041 |a eng 
044 |b السعودية 
100 |9 524579  |a Sibai, Fadi N.  |e Author 
245 |a Performance Modeling And Analysis Of Parallel Gaussian Elimination On Multi-core Computers 
260 |b جامعة الملك سعود  |c 2014 
300 |a 41 - 54 
336 |a بحوث ومقالات  |b Article 
520 |b Gaussian elimination is used in many applications and in particular in the solution of systems of linear equations. This paper presents mathematical performance models and analysis of four parallel Gaussian Elimination methods (precisely the Original method and the new meet in the Middle –MiM– algorithms and their variants with SIMD vectorization) on multi-core systems. Analytical performance models of the four methods are formulated and presented followed by evaluations of these models with modern multi-core systems’ operation latencies. Our results reveal that the four methods generally exhibit good performance scaling with increasing matrix size and number of cores. SIMD vectorization only makes a large difference in performance for low number of cores. For a large matrix size (nP 16 K), the performance difference between the MiM and Original methods falls from 16• with four cores to 4• with 16 K cores. The efficiencies of all four methods are low with 1 K cores or more stressing a major problem of multi-core systems where the network-on-chip and memory latencies are too high in relation to basic arithmetic operations. Thus Gaussian Elimination can greatly benefit from the resources of multi-core systems, but higher performance gains can be achieved if multi-core systems can be designed with lower memory operation, synchronization, and interconnect communication latencies, requirements of utmost importance and challenge in the exascale computing age. 
653 |a علوم الحاسوب  |a نظم المعلومات  |a الخوارزميات 
692 |b Gaussian Elimination  |b Multi-core Computing  |b Performance Modeling And Analysis 
773 |c 005  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 001  |m مج26, ع1  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 026  |x 1319-1578 
856 |u 0584-026-001-005.pdf 
930 |d y  |p y 
995 |a science 
999 |c 973012  |d 973012 

عناصر مشابهة