» IPM HPC Laboratory

Jan 03

7th IPM-HPC WorkShop on Multi-Core Systems & GPU and it’s Application in HPC

عناوین
مقدمه ای بر معماری کامپیوتر و معماری موازی
زبان های برنامه نویسی پردازنده های چند هسته ای
مدل برنامه نوسی پردازنده های گرافیکی
 CUDA، OpenMP کارگاه برنامه نویسی
  مقدمه‌ای بر پول‌های مجازی و نیازآن به پردازش سریع
مقدمه‌ای بر تنوع تکنولوژی‌های موجود در صنعت کامپیوتر و کارایی آن‌ها
لطفا در صورت امکان جهت هماهنگی و آموزش بهتر، با خود لپتاپ بیاورید
تاریخ و محل برگزاری
تاریخ:  چهارشنبه و پنجشنبه – 25 و 26 بهمن ماه 1396
تهران، خيابان شهيد لواساني (ساختمان فرمانيه)، بعد از برج کوه نور

مبلغ ثبت نام:
دانشجویان: 100 هزار تومان
اساتید و پژوهشگران دانشگاهی: 150 هزار تومان
کارشناسان صنعت: 200 هزار تومان

مهلت ثبت نام: (20) 24 بهمن ماه 1396

تنها کسانی که فرآیند ثبت‌نام را تکمیل کنند در لیست نهایی قرار می‌گیرند
علاقه مندان برای ثبت‌نام می‌توانند به اینجا  مراجعه کنند

(اطلاعات حساب (جهت واریز وجه ثبت نام

شماره حساب : 2172149001004
بانک عامل : ملی
شعبه : نیاوران
  (IPM) به نام: پژوهشکده دانش های بنیادی
عنوان پرداخت : هفتمین کارگاه برنامه نویسی سیستم های چند هسته‌ای و پردازنده های گرافیکی و کاربردهای آن در پردازش سریع
 مهلت پرداخت :  96/11/24
همچنین اصل رسید و در صورت دانشجو بودن، کارت دانشجویی خود را در روز برگزاری کارگاه به همراه داشته باشید
مشخصات تماس
  تهران، خيابان شهيد لواساني (ساختمان فرمانيه)، بعد از برج کوه نور
 http://hpc.ipm.ac.ir:وب سایت
hpc (- at -) ipm .ir :رایانامه
شماره تماس: 982124509409

Jan 31

6th IPM-HPC WorkShop on Multi-Core Systems and it’s Application in Big Data

عناوین
مقدمه ای بر معماری کامپیوتر و معماری موازی
زبان های برنامه نویسی پردازنده های چند هسته ای
مدل برنامه نوسی پردازنده های گرافیکی
 CUDA، OpenMP کارگاه برنامه نویسی
  Hadoopبا استفاده از بستر منبع‌باز Map-Reduce  مدل برنامه‌نویسی
OpenStack راهکارهای پیاده‌سازی مرکز داده‌ابری با استفاده از
تاریخ و محل برگزاری
(تاریخ:  چهارشنبه و پنجشنبه – 4 و 5 اسفند (9:00 – 17:00
تهران، خيابان شهيد لواساني (ساختمان فرمانيه)، بعد از برج کوه نور
ثبت نام
هزینه ثبت نام دانشجویی 1.000.000 ریال (معادل یکصد هزار تومان) و هزینه ثبت نام آزاد 2.000.000 ریال است.
تنها کسانی که فرآیند ثبت‌نام را تکمیل کنند در لیست نهایی قرار می‌گیرند.
علاقه مندان برای ثبت‌نام می‌توانند به اینجا مراجعه کنند

اطلاعات حساب (جهت واریز وجه ثبت نام)

شماره حساب : 2172149001004
بانک عامل : ملی
شعبه : نیاوران
به نام: پژوهشکده دانش های بنیادی  (IPM)
عنوان پرداخت : ششمین کارسوق برنامه نویسی چند هسته ای
 مهلت پرداخت :  95/12/02
همچنین اصل رسید و در صورت دانشجو بودن، کارت دانشجویی خود را در روز برگزاری کارسوق به همراه داشته باشید
مشخصات تماس
  تهران، خيابان شهيد لواساني (ساختمان فرمانيه)، بعد از برج کوه نور
 http://hpc.ipm.ac.ir:وب سایت
hpc (- at -) ipm .ir :رایانامه
شماره تماس: 982124509409
 .مراجعه کنید ftp://cluster.hpc.ipm.ac.ir/ برای دانلود اسلایدهای ورک‌شاپ به آدرس

Jan 05

Being In First Position In Memocode 2016 Design Contest

14th ACM-IEEE International Conference On Formal Methods and Models For System Design

Indian Institute of Technology, Kanpur

November 18-20, 2016

As in previous years, The IPM HPC’s team won the honor of Memocode design contest 2016. Our team not only defended its previous years rank in performance/cost class, but also we won the performance class.

IPM-HPC Team members: Saeid Rahmani, Armin Ahmadzadeh, Omid Hajihassani, SeyedPooya Mirhosseini, and Saeid Gorgin

Institute for Research in Fundamental Sciences (IPM), Iran

Contest Problem: The MEMOCODE’16 will include a design contest, which will pose a computational challenge that participants may solve using hardware or software on FPGAs, GPUs, and CPUs. The conference will sponsor at least one prize with a monetary award for the contest winners. The 2016 challenge is K-means clustering that is an unsupervised method for clustering multidimensional data points, aiming to partition the points into “K” subgroups (clusters) that are similar. This is used in a variety of applications such as data mining, image segmentation, medical imaging, and bioinformatics.

 

IPM-HPC Solution:  Our method makes exhaustive use of four High throughput GPU and hides memory latency. The 2016 MEMOCODE Design Contest was to efficiently compute k-means clustering on a large multidimensional data set. We performed effective optimizations involving the algorithmic structure and parallelism. We implemented our design using Intel Xeon E5 CPUs and NVIDIA GTX 980 GPUs. Our overall best result computed the solution in 106ms using four GPUs. In terms of cost normalized results, our best solution was the 2x GPU implementation, which was only 1.5x slower than the 4x GPU solution, at half the cost. The IPM team’s implementation strategy involved careful parallelization of the problem across available platforms, as well as optimization of the arithmetic required by the problem. Moreover, The solution was based on Lloyd’s algorithm.

our scientific article will be published soon.

Jan 31

Winner in Memocode 2015 Design Contest

13th ACM-IEEE International Conference on Formal Methods and Models for System Design
The University of Texas at Austin
September 21-23, 2015

The winners of 2015 MEMOCODE Design Contest:

Rank in Best Cost-Normalized Performance and Highest Performance class: 2- IPM-HPC, 1- Tokyo University

IPM-HPC Team members: Armin Ahmadzadeh, Ehsan Montahaie, Milad Ghafouri, Reza Mirzaei, Saied Rahmani, Farzad Sharif Bakhtiar, Mohsen Gavahi, Rashid Zamanshoar, Hanie Ghasemi, Kianoush Jafari, Saeid Gorgin

Institute for Research in Fundamental Sciences (IPM), Iran

Contest Problem: As in previous years, MEMOCODE’15 will include a design contest, which will pose a computational challenge that participants may solve using hardware or software on FPGAs, GPUs, and CPUs. The conference will sponsor at least one prize with a monetary award for the contest winners. the 2015 challenge is continuous skyline computation that given dataset is not constant and it changes over time. the aim of this contest is to implement a system to efficiently compute the continuous skyline of a large dynamic dataset.

IPM-HPC Solution:  Our method makes exhaustive use of CPU and minimizes memory access. we present an efficient parallel continuous Skyline approach. In our suggested method, the dataset points are sorted and pruned based on Manhattan distance. Moreover, we use several optimization methods to optimize memory usage in comparison with naïve implementation. In addition, besides the applied conventional parallelization methods, we partition the time steps based on the number of available cores. The experimental results for a data set that contains 800k points with 7 dimensions show considerable speedup.for more information see the paper.

Jan 13

Winner in MEMOCODE 2014 Design Contest

Winner in Memocode 2014-Hardware Contest

Switzerland, EPFL in Lausanne
Rank in Best Cost-Normalized Performance class: 1 – IPM-HPC-MultiCore (winner), 2 and 3- IPM-HPC-ManyCore (GPU), 4- Iowa State University (USA)
Rank in Highest Performance class: 2- IPMH-HPC, 1- Iowa State University (USA) (winner)
Our team defended its previous years rank in performance/cost class.

IPM-HPC Team members:  Armin Ahmadzadeh, Reza Mirzaei, Hatef Madani, Mohammad Shobeiri, Mahsa Sadeghi, Mohsen Gavahi, Kianoush Jafari, Mohsen Mahmoudi Aznaveh, and Saeid Gorgin.

Contest Problem:The 2014 problem for The MEMOCODE hardware contest problem is k-Nearest Neighbor search using the Mahalanobis distance metric. Given a data set of points in multidimensional space, the goal is to find the k points that are nearest to any given point in that space.

IPM-HPC Solution: Regarding to the MEMOCODE chair email, there were 10 excellent solutions submitted, utilizing a range of algorithmic as well as system-level optimizations, targeting FPGA, GPU as well as multi-core platforms. Our method makes exhaustive use of CPU and minimizes memory access. This method is the winner of Best Cost-Normalized Performance of MEMOCODE contest design 2014 and is 616X faster than other implementation of the contest. See this paper for more detail.

 

Winner in Memocode 2014-Software Contest

Switzerland, EPFL in Lausanne
Rank in Software contest: 2 – IPM-HPC, 1-U.S. Army Research, Development and Engineering (USA) – Winner

IPM-HPC Team members:Nariman Eskandari, Hatef Madani, Armin Ahmadzadeh, Saied Gorgin, Mohsen Mahmoudi Aznaveh.

Contest Problem:The 2014 problem for The MEMOCODE software contest problem is to make the emulator run even faster on the Raspberry PI by proposing software solutions. These contest used specific emulator for Space Invaders game written for 8080 processor.

IPM-HPC Solution: Regarding to the MEMOCODE software contest report, there were three contestants were successful in completing this software design contest. These contestants employed a variety of techniques to both discover and optimize performance bottlenecks in the emulator. Our method is focused in optimizing function calls that were frequently invoked, and re-organizing data-structures to better match the underlying Raspberry Pi’s hardware architecture. These approach is 2.5 times faster over the conventional implementation of the contest. See this paper for more detail.

Aug 22

IPM Two-Day Workshop on Thinking in GPU Programming







 

 

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