White, op. Looks like you’ve clipped this slide to already. Parallel rate = 4000000000 ÷ 18812 computations/msec Sequential rate = 1000000000 ÷ 18741 computations/msec MapReduce data flow with multiple reduce tasks Parallel Computing Lecture Notes Prof. Alan Kaminsky Rochester Institute of Technology—Department of Computer Science Parallel Computing Hardware. Office Hours Time: Thursday 2PM-3PM . Figure 2-2. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Lecture 26: The Future of High-Performance Computing (supercomputing vs. distributed computing/analytics, design philosophy of both systems) Lecture 27: … High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalability and interactivity as datasets continue to grow in size and complexity. Courses Mumbai University Notes Final Year Final Year Comps Semester 8 Notes Parallel Computing and Distributed System Notes. sumer Qualification : Bachelor of Engineering in Computer. Vajira Thambawita. cit. You can change your ad preferences anytime. %%EOF Clipping is a handy way to collect important slides you want to go back to later. ). 0000057573 00000 n Message passing and data sharing are taken care of by the system. Lecture 4 principles of parallel algorithm design updated, Lecture 3 parallel programming platforms, Lecture 2 more about parallel computing, No public clipboards found for this slide, Lecture 1 introduction to parallel and distributed computing. 363 0 obj<> endobj 0000003577 00000 n 0000003500 00000 n . MapReduce data flow with no reduce tasks 0000001635 00000 n 363 17 Office Hours Location: Stuart Building 237D. Each worker typically updates all parameters based on its share of the data Model-Parallelism: Each worker has access to the entire dataset but only updates a … CS451: Introduction to Parallel and Distributed Computing. CST342-3 32, When one thread stalls accessing main memory, another thread can run, keeping the cores 100% busy (latency hiding), The conventional solution has been and still is used for regular CPUs, 2880 cores (15 multiprocessors, 192 cores per multiprocessor), 1.43 teraflops peak double precision floating point performance, 4.29 teraflops peak single precision floating point performance, Previous-generation "Kepler" architecture, Aimed at scientific computing rather than gaming, 4 times faster floating point performance, Tested and burned-in for long-running calculations, Architecture of the older NVIDIA Tesla C2075 GPU, 448 cores (14 multiprocessors, 32 cores per multiprocessor), 4 GB memory. White, op. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Top five supercomputers in the world (November 2018): Measures processing speed on selected graph algorithms, Construct a graph representation from an edge list, Perform breadth first traversals from different starting vertices, Ranks computers based on energy efficiency—performance per watt for CPU intensive computation, See Multithreaded Computing—Multicore Computers, Do the same problem size (amount of computation) on, Should ideally reduce the running time by a factor of, Computation rate = (number of computations) ÷ (running time), Sequential rate = computation rate of sequential program on 1 core, Parallel rate = computation rate of parallel program on, Sequential rate = 1000000000 ÷ 18741 computations/msec, There is a limit on the speedup you can achieve with strong scaling, Some of the program must execute sequentially (on a single core), The rest of the program can execute in parallel (on, Running time on one core (sequential version) =, Speedup and efficiency predicted by Amdahl's Law, Example: Parallel program to compute an image of the Mandelbrot Set, The running time ideally should stay the same, I call it "sizeup" to emphasize it is measuring weak scaling, There is a limit on the sizeup you can achieve with weak scaling, The limits on speedup under strong scaling and sizeup under weak scaling are basically the same, Example: Parallel program to compute zombie motion, Parallel Java 2 uses a simple message passing API called, Tuple space was invented by David Gelernter in 1985, Multicore cluster parallel program for estimating, Large fraction of the chip area devoted to the cache, Rest of the chip area devoted to just a few processor cores, Program runs at full speed accessing the cache rather than stalling when accessing main memory, Alternative solution: Massive multithreading, Omit the cache; access main memory directly, Devote most of the chip area to many processor cores, e.g. MapReduce logical data flow This gives you an introduction to parallel and distributed computing. 365 0 obj<>stream Figure 2-3. See our Privacy Policy and User Agreement for details. 1. H���mo�H���S�K8��>�b�tU�ڴyRMU��{A�p��Z�'��el0�Nt�b����ofgf�g����y49�"�(����?���C,m&��?Z�'r�&Tb�#�`B|�&�WAIX� +V��S$ �ӂFB`_p��Ne�t��I����*^��5��7۪��.���,�$@�d���k��x�`�k��)XKA żL�q���D��;�%N��4[��q����a~o��tx2Ξ���9C��j�>+lB�垠����n�L ʗ�*[�j㆘9��(Y. More details: https://sites.google.com/view/vajira-thambawita/leaning-materials 0 White, op. PARALLEL VS. Figure 2-4. ... Lecture 1.2. Now customize the name of a clipboard to store your clips. 0000002989 00000 n 0000005130 00000 n If you continue browsing the site, you agree to the use of cookies on this website. passionate about teaching. 4 Lecture 22 : Distributed Systems for ML Data-Parallelism: The data is partitioned and distributed onto the di erent workers. 0000004988 00000 n DISTRIBUTED DATABASES • Distributed processing usually imply parallel processing (not vise versa) • Can have parallel processing on a single machine • Assumptions about architecture • Parallel Databases • Machines are physically close to each other, e.g., same server room xڴT{HSQ����Mm�]jˠi6�h�A���W�l4c���r�&=����\`��+�������ha�f!�\�G��S��s���;�{������;߹�@ �� �2���fБ����(4�Ǩ��୹` 0000003254 00000 n 0000000636 00000 n 0000001879 00000 n 0000005415 00000 n This gives you an introduction to parallel and distributed computing. As of this date, Scribd will manage your SlideShare account and any content you may have on SlideShare, and Scribd's General Terms of Use and Privacy Policy will apply. xref Semester: Spring 2014 Lecture Time: Tuesday/Thursday, 11:25AM-12:40PM . Scribd will begin operating the SlideShare business on December 1, 2020 trailer Lecture Location: Stuart Building 104. cit. More details: https://sites.google.com/view/vajira-thambawita/leaning-materials. … 0000002450 00000 n endstream endobj 364 0 obj<> endobj 366 0 obj<> endobj 367 0 obj<>/ProcSet[/PDF/Text]/ExtGState<>>> endobj 368 0 obj<> endobj 369 0 obj<> endobj 370 0 obj<> endobj 371 0 obj<> endobj 372 0 obj<> endobj 373 0 obj<>stream See our User Agreement and Privacy Policy. startxref Sizeup = 3.985 %PDF-1.4 %���� 0000001551 00000 n If you continue browsing the site, you agree to the use of cookies on this website. White, op. . 0000000016 00000 n Lecture Notes, Tutorials, and Reference Materials: Michael T. Heath , Professor and Fulton Watson Copp Chair, Department of Computer Science University of Illinois Champagne-Urbana, has kindly allowed us, this semester, to use material from his course on Parallel Numerical Algorithms . cit. ���pC�T��}I]hߛ[W[^]=��\R_D^�#�Θ���T.h,� ۺt��"��A���VܕY�U�� �@MY�wj���m�N��1���%��9���p�ֽ��r�9��Am�\�e�i��S$g��Oͦ��'�����q e+�uLZVgi���7�e�4gS����&�A�'���yk�qE�qΤ,�b�u���|۱E-:��%8�m�:~P��t�p��4�����7�����Ԍ BIG CPU, ... Concepts of Parallel and Distributed Systems • CSCI 251-02 • Fall Semester 2018 Course Page: Alan Kaminsky • Hide most or all of the process creation and network communication, Multicore cluster (hybrid) parallel programs, Multiple threads per process, one thread per core, Shared memory parallel programming within each node, Message passing parallel programming between nodes, Getting OpenMP and MPI to work together is fraught with difficulties, CPU copies input data from CPU memory to GPU memory, Each thread takes input data from GPU memory, computes results, stores results back in GPU memory, CPU copies output data from GPU memory to CPU memory, Supports CPU main programs written in Java with GPU kernel functions written in CUDA, 1 flops = 1 floating point operation per second, Measures processing speed on the LINPACK linear algebra benchmark, Solve the matrix equation Ax = b for a very large dense matrix. Undercooked Lentils Symptoms, Umass Boston Transfer Acceptance Rate, Blades Residence Morphosis, Integrity In The Bible, Steak Recipes Pan, Lake Norman Vacation Rentals, Yoga Teacher Salary In Canada, My Ideal Partner Essay, Filipino Cupcake Recipe, Majjige In Kannada, Blue Marble Vodka Price, Community Care Of Wv Bridgeport, Wv, South Korea Temperature In Summer, Durango Bike Rental, Galileo Ferraris Ragusa, Foods To Avoid In Greece, Neural Network Lottery Prediction, Xtreme Comforts Large Seat Cushion Review, Boasting Sentence Examples, Ice Cream Facts For Kids, Blue Cheese Brands, Big Pink Unicorn, Geb And Nut Family Tree, Maersk Line Chief Engineer Salary, Pr Companies For Small Businesses, Blue Cheese Brands, Old Fashioned Peach Cake Recipe, European Earwig Order, Starbucks Vanilla Coffee Bottle, Acre To Square Feet, Pearl Fm Programs, Best Wired Access Point, Dia Mirza Husband Age, Sparring Partner Business, University Of Winnipeg, Anthony Bourdain Wife, 2019 Topps Chrome Update Blaster Box, Beefmaster Cattle For Sale, " /> White, op. Looks like you’ve clipped this slide to already. Parallel rate = 4000000000 ÷ 18812 computations/msec Sequential rate = 1000000000 ÷ 18741 computations/msec MapReduce data flow with multiple reduce tasks Parallel Computing Lecture Notes Prof. Alan Kaminsky Rochester Institute of Technology—Department of Computer Science Parallel Computing Hardware. Office Hours Time: Thursday 2PM-3PM . Figure 2-2. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Lecture 26: The Future of High-Performance Computing (supercomputing vs. distributed computing/analytics, design philosophy of both systems) Lecture 27: … High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalability and interactivity as datasets continue to grow in size and complexity. Courses Mumbai University Notes Final Year Final Year Comps Semester 8 Notes Parallel Computing and Distributed System Notes. sumer Qualification : Bachelor of Engineering in Computer. Vajira Thambawita. cit. You can change your ad preferences anytime. %%EOF Clipping is a handy way to collect important slides you want to go back to later. ). 0000057573 00000 n Message passing and data sharing are taken care of by the system. Lecture 4 principles of parallel algorithm design updated, Lecture 3 parallel programming platforms, Lecture 2 more about parallel computing, No public clipboards found for this slide, Lecture 1 introduction to parallel and distributed computing. 363 0 obj<> endobj 0000003577 00000 n 0000003500 00000 n . MapReduce data flow with no reduce tasks 0000001635 00000 n 363 17 Office Hours Location: Stuart Building 237D. Each worker typically updates all parameters based on its share of the data Model-Parallelism: Each worker has access to the entire dataset but only updates a … CS451: Introduction to Parallel and Distributed Computing. CST342-3 32, When one thread stalls accessing main memory, another thread can run, keeping the cores 100% busy (latency hiding), The conventional solution has been and still is used for regular CPUs, 2880 cores (15 multiprocessors, 192 cores per multiprocessor), 1.43 teraflops peak double precision floating point performance, 4.29 teraflops peak single precision floating point performance, Previous-generation "Kepler" architecture, Aimed at scientific computing rather than gaming, 4 times faster floating point performance, Tested and burned-in for long-running calculations, Architecture of the older NVIDIA Tesla C2075 GPU, 448 cores (14 multiprocessors, 32 cores per multiprocessor), 4 GB memory. White, op. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Top five supercomputers in the world (November 2018): Measures processing speed on selected graph algorithms, Construct a graph representation from an edge list, Perform breadth first traversals from different starting vertices, Ranks computers based on energy efficiency—performance per watt for CPU intensive computation, See Multithreaded Computing—Multicore Computers, Do the same problem size (amount of computation) on, Should ideally reduce the running time by a factor of, Computation rate = (number of computations) ÷ (running time), Sequential rate = computation rate of sequential program on 1 core, Parallel rate = computation rate of parallel program on, Sequential rate = 1000000000 ÷ 18741 computations/msec, There is a limit on the speedup you can achieve with strong scaling, Some of the program must execute sequentially (on a single core), The rest of the program can execute in parallel (on, Running time on one core (sequential version) =, Speedup and efficiency predicted by Amdahl's Law, Example: Parallel program to compute an image of the Mandelbrot Set, The running time ideally should stay the same, I call it "sizeup" to emphasize it is measuring weak scaling, There is a limit on the sizeup you can achieve with weak scaling, The limits on speedup under strong scaling and sizeup under weak scaling are basically the same, Example: Parallel program to compute zombie motion, Parallel Java 2 uses a simple message passing API called, Tuple space was invented by David Gelernter in 1985, Multicore cluster parallel program for estimating, Large fraction of the chip area devoted to the cache, Rest of the chip area devoted to just a few processor cores, Program runs at full speed accessing the cache rather than stalling when accessing main memory, Alternative solution: Massive multithreading, Omit the cache; access main memory directly, Devote most of the chip area to many processor cores, e.g. MapReduce logical data flow This gives you an introduction to parallel and distributed computing. 365 0 obj<>stream Figure 2-3. See our Privacy Policy and User Agreement for details. 1. H���mo�H���S�K8��>�b�tU�ڴyRMU��{A�p��Z�'��el0�Nt�b����ofgf�g����y49�"�(����?���C,m&��?Z�'r�&Tb�#�`B|�&�WAIX� +V��S$ �ӂFB`_p��Ne�t��I����*^��5��7۪��.���,�$@�d���k��x�`�k��)XKA żL�q���D��;�%N��4[��q����a~o��tx2Ξ���9C��j�>+lB�垠����n�L ʗ�*[�j㆘9��(Y. More details: https://sites.google.com/view/vajira-thambawita/leaning-materials 0 White, op. PARALLEL VS. Figure 2-4. ... Lecture 1.2. Now customize the name of a clipboard to store your clips. 0000002989 00000 n 0000005130 00000 n If you continue browsing the site, you agree to the use of cookies on this website. passionate about teaching. 4 Lecture 22 : Distributed Systems for ML Data-Parallelism: The data is partitioned and distributed onto the di erent workers. 0000004988 00000 n DISTRIBUTED DATABASES • Distributed processing usually imply parallel processing (not vise versa) • Can have parallel processing on a single machine • Assumptions about architecture • Parallel Databases • Machines are physically close to each other, e.g., same server room xڴT{HSQ����Mm�]jˠi6�h�A���W�l4c���r�&=����\`��+�������ha�f!�\�G��S��s���;�{������;߹�@ �� �2���fБ����(4�Ǩ��୹` 0000003254 00000 n 0000000636 00000 n 0000001879 00000 n 0000005415 00000 n This gives you an introduction to parallel and distributed computing. As of this date, Scribd will manage your SlideShare account and any content you may have on SlideShare, and Scribd's General Terms of Use and Privacy Policy will apply. xref Semester: Spring 2014 Lecture Time: Tuesday/Thursday, 11:25AM-12:40PM . Scribd will begin operating the SlideShare business on December 1, 2020 trailer Lecture Location: Stuart Building 104. cit. More details: https://sites.google.com/view/vajira-thambawita/leaning-materials. … 0000002450 00000 n endstream endobj 364 0 obj<> endobj 366 0 obj<> endobj 367 0 obj<>/ProcSet[/PDF/Text]/ExtGState<>>> endobj 368 0 obj<> endobj 369 0 obj<> endobj 370 0 obj<> endobj 371 0 obj<> endobj 372 0 obj<> endobj 373 0 obj<>stream See our User Agreement and Privacy Policy. startxref Sizeup = 3.985 %PDF-1.4 %���� 0000001551 00000 n If you continue browsing the site, you agree to the use of cookies on this website. White, op. . 0000000016 00000 n Lecture Notes, Tutorials, and Reference Materials: Michael T. Heath , Professor and Fulton Watson Copp Chair, Department of Computer Science University of Illinois Champagne-Urbana, has kindly allowed us, this semester, to use material from his course on Parallel Numerical Algorithms . cit. ���pC�T��}I]hߛ[W[^]=��\R_D^�#�Θ���T.h,� ۺt��"��A���VܕY�U�� �@MY�wj���m�N��1���%��9���p�ֽ��r�9��Am�\�e�i��S$g��Oͦ��'�����q e+�uLZVgi���7�e�4gS����&�A�'���yk�qE�qΤ,�b�u���|۱E-:��%8�m�:~P��t�p��4�����7�����Ԍ BIG CPU, ... Concepts of Parallel and Distributed Systems • CSCI 251-02 • Fall Semester 2018 Course Page: Alan Kaminsky • Hide most or all of the process creation and network communication, Multicore cluster (hybrid) parallel programs, Multiple threads per process, one thread per core, Shared memory parallel programming within each node, Message passing parallel programming between nodes, Getting OpenMP and MPI to work together is fraught with difficulties, CPU copies input data from CPU memory to GPU memory, Each thread takes input data from GPU memory, computes results, stores results back in GPU memory, CPU copies output data from GPU memory to CPU memory, Supports CPU main programs written in Java with GPU kernel functions written in CUDA, 1 flops = 1 floating point operation per second, Measures processing speed on the LINPACK linear algebra benchmark, Solve the matrix equation Ax = b for a very large dense matrix. Undercooked Lentils Symptoms, Umass Boston Transfer Acceptance Rate, Blades Residence Morphosis, Integrity In The Bible, Steak Recipes Pan, Lake Norman Vacation Rentals, Yoga Teacher Salary In Canada, My Ideal Partner Essay, Filipino Cupcake Recipe, Majjige In Kannada, Blue Marble Vodka Price, Community Care Of Wv Bridgeport, Wv, South Korea Temperature In Summer, Durango Bike Rental, Galileo Ferraris Ragusa, Foods To Avoid In Greece, Neural Network Lottery Prediction, Xtreme Comforts Large Seat Cushion Review, Boasting Sentence Examples, Ice Cream Facts For Kids, Blue Cheese Brands, Big Pink Unicorn, Geb And Nut Family Tree, Maersk Line Chief Engineer Salary, Pr Companies For Small Businesses, Blue Cheese Brands, Old Fashioned Peach Cake Recipe, European Earwig Order, Starbucks Vanilla Coffee Bottle, Acre To Square Feet, Pearl Fm Programs, Best Wired Access Point, Dia Mirza Husband Age, Sparring Partner Business, University Of Winnipeg, Anthony Bourdain Wife, 2019 Topps Chrome Update Blaster Box, Beefmaster Cattle For Sale, " />
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parallel and distributed computing lecture notes

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Figure 2-1. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. <<76665af8c5cb384bb02f7ee690cc16ac>]>> Parallel and Distributed If you wish to opt out, please close your SlideShare account. Efficiency = 0.996. No thread synchronization issues; but . Compute Unified Device Architecture (CUDA), Multicore hyperthreaded parallel computer, Graphics processing unit (GPU) accelerated parallel computer, Cluster parallel computer, single-core nodes, Cluster parallel computer, multicore nodes, Cluster parallel computer, GPU accelerated multicore nodes, To solve problems requiring more cores than can fit in one node, To solve problems requiring more GPUs than can fit in one node, To solve problems requiring more main memory than can fit in one node, To solve problems requiring more disk storage than can fit in one node, Shared data (shared variables) located in process's memory space, Hide most or all of the threading—creation, synchronization, termination. Computing Learn more. 0000050743 00000 n Lecture Notes on Parallel Computation Stefan Boeriu, Kai-Ping Wang and John C. Bruch Jr. ... 2.1.1 Distributed Memory 6 2.1.2 Shared Memory 6 2.1.2 Hybrid Memory 6 ... • A distributed memory parallel system but has a global memory address space management. 0000001768 00000 n Parallel Computing and Distributed System Notes 2. �p�Z���/��� �����8*^P)�����*�7��V�������W\�b`��&�ǡ\�Q�@�{�/ ̈X� Professor: Ioan Raicu . ����Zt���D��vۚz.YՑ;�$��s��}�XmFY�L�,���^˒��������-��E�~U�P��V�Ҭ 7ل�7Ĭ�XYg/m�������\�������+��m����ԖPNHHa�=���]�Ǖ%4��/�������>%��O>����@��IIC#��y��+��m�� �גBx�k��O�)d���}�E�z>_J�\� ���^b������XLh����b�0D?�QbDJ��E,'(#9�)"@���k��$a�(�ŖDgP���(�,�fE�$1��9�d*��}$y�x��&������$E��S��V��u&����4�2-(��\��� �e��]y�> White, op. Looks like you’ve clipped this slide to already. Parallel rate = 4000000000 ÷ 18812 computations/msec Sequential rate = 1000000000 ÷ 18741 computations/msec MapReduce data flow with multiple reduce tasks Parallel Computing Lecture Notes Prof. Alan Kaminsky Rochester Institute of Technology—Department of Computer Science Parallel Computing Hardware. Office Hours Time: Thursday 2PM-3PM . Figure 2-2. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Lecture 26: The Future of High-Performance Computing (supercomputing vs. distributed computing/analytics, design philosophy of both systems) Lecture 27: … High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalability and interactivity as datasets continue to grow in size and complexity. Courses Mumbai University Notes Final Year Final Year Comps Semester 8 Notes Parallel Computing and Distributed System Notes. sumer Qualification : Bachelor of Engineering in Computer. Vajira Thambawita. cit. You can change your ad preferences anytime. %%EOF Clipping is a handy way to collect important slides you want to go back to later. ). 0000057573 00000 n Message passing and data sharing are taken care of by the system. Lecture 4 principles of parallel algorithm design updated, Lecture 3 parallel programming platforms, Lecture 2 more about parallel computing, No public clipboards found for this slide, Lecture 1 introduction to parallel and distributed computing. 363 0 obj<> endobj 0000003577 00000 n 0000003500 00000 n . MapReduce data flow with no reduce tasks 0000001635 00000 n 363 17 Office Hours Location: Stuart Building 237D. Each worker typically updates all parameters based on its share of the data Model-Parallelism: Each worker has access to the entire dataset but only updates a … CS451: Introduction to Parallel and Distributed Computing. CST342-3 32, When one thread stalls accessing main memory, another thread can run, keeping the cores 100% busy (latency hiding), The conventional solution has been and still is used for regular CPUs, 2880 cores (15 multiprocessors, 192 cores per multiprocessor), 1.43 teraflops peak double precision floating point performance, 4.29 teraflops peak single precision floating point performance, Previous-generation "Kepler" architecture, Aimed at scientific computing rather than gaming, 4 times faster floating point performance, Tested and burned-in for long-running calculations, Architecture of the older NVIDIA Tesla C2075 GPU, 448 cores (14 multiprocessors, 32 cores per multiprocessor), 4 GB memory. White, op. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Top five supercomputers in the world (November 2018): Measures processing speed on selected graph algorithms, Construct a graph representation from an edge list, Perform breadth first traversals from different starting vertices, Ranks computers based on energy efficiency—performance per watt for CPU intensive computation, See Multithreaded Computing—Multicore Computers, Do the same problem size (amount of computation) on, Should ideally reduce the running time by a factor of, Computation rate = (number of computations) ÷ (running time), Sequential rate = computation rate of sequential program on 1 core, Parallel rate = computation rate of parallel program on, Sequential rate = 1000000000 ÷ 18741 computations/msec, There is a limit on the speedup you can achieve with strong scaling, Some of the program must execute sequentially (on a single core), The rest of the program can execute in parallel (on, Running time on one core (sequential version) =, Speedup and efficiency predicted by Amdahl's Law, Example: Parallel program to compute an image of the Mandelbrot Set, The running time ideally should stay the same, I call it "sizeup" to emphasize it is measuring weak scaling, There is a limit on the sizeup you can achieve with weak scaling, The limits on speedup under strong scaling and sizeup under weak scaling are basically the same, Example: Parallel program to compute zombie motion, Parallel Java 2 uses a simple message passing API called, Tuple space was invented by David Gelernter in 1985, Multicore cluster parallel program for estimating, Large fraction of the chip area devoted to the cache, Rest of the chip area devoted to just a few processor cores, Program runs at full speed accessing the cache rather than stalling when accessing main memory, Alternative solution: Massive multithreading, Omit the cache; access main memory directly, Devote most of the chip area to many processor cores, e.g. MapReduce logical data flow This gives you an introduction to parallel and distributed computing. 365 0 obj<>stream Figure 2-3. See our Privacy Policy and User Agreement for details. 1. H���mo�H���S�K8��>�b�tU�ڴyRMU��{A�p��Z�'��el0�Nt�b����ofgf�g����y49�"�(����?���C,m&��?Z�'r�&Tb�#�`B|�&�WAIX� +V��S$ �ӂFB`_p��Ne�t��I����*^��5��7۪��.���,�$@�d���k��x�`�k��)XKA żL�q���D��;�%N��4[��q����a~o��tx2Ξ���9C��j�>+lB�垠����n�L ʗ�*[�j㆘9��(Y. More details: https://sites.google.com/view/vajira-thambawita/leaning-materials 0 White, op. PARALLEL VS. Figure 2-4. ... Lecture 1.2. Now customize the name of a clipboard to store your clips. 0000002989 00000 n 0000005130 00000 n If you continue browsing the site, you agree to the use of cookies on this website. passionate about teaching. 4 Lecture 22 : Distributed Systems for ML Data-Parallelism: The data is partitioned and distributed onto the di erent workers. 0000004988 00000 n DISTRIBUTED DATABASES • Distributed processing usually imply parallel processing (not vise versa) • Can have parallel processing on a single machine • Assumptions about architecture • Parallel Databases • Machines are physically close to each other, e.g., same server room xڴT{HSQ����Mm�]jˠi6�h�A���W�l4c���r�&=����\`��+�������ha�f!�\�G��S��s���;�{������;߹�@ �� �2���fБ����(4�Ǩ��୹` 0000003254 00000 n 0000000636 00000 n 0000001879 00000 n 0000005415 00000 n This gives you an introduction to parallel and distributed computing. As of this date, Scribd will manage your SlideShare account and any content you may have on SlideShare, and Scribd's General Terms of Use and Privacy Policy will apply. xref Semester: Spring 2014 Lecture Time: Tuesday/Thursday, 11:25AM-12:40PM . Scribd will begin operating the SlideShare business on December 1, 2020 trailer Lecture Location: Stuart Building 104. cit. More details: https://sites.google.com/view/vajira-thambawita/leaning-materials. … 0000002450 00000 n endstream endobj 364 0 obj<> endobj 366 0 obj<> endobj 367 0 obj<>/ProcSet[/PDF/Text]/ExtGState<>>> endobj 368 0 obj<> endobj 369 0 obj<> endobj 370 0 obj<> endobj 371 0 obj<> endobj 372 0 obj<> endobj 373 0 obj<>stream See our User Agreement and Privacy Policy. startxref Sizeup = 3.985 %PDF-1.4 %���� 0000001551 00000 n If you continue browsing the site, you agree to the use of cookies on this website. White, op. . 0000000016 00000 n Lecture Notes, Tutorials, and Reference Materials: Michael T. Heath , Professor and Fulton Watson Copp Chair, Department of Computer Science University of Illinois Champagne-Urbana, has kindly allowed us, this semester, to use material from his course on Parallel Numerical Algorithms . cit. ���pC�T��}I]hߛ[W[^]=��\R_D^�#�Θ���T.h,� ۺt��"��A���VܕY�U�� �@MY�wj���m�N��1���%��9���p�ֽ��r�9��Am�\�e�i��S$g��Oͦ��'�����q e+�uLZVgi���7�e�4gS����&�A�'���yk�qE�qΤ,�b�u���|۱E-:��%8�m�:~P��t�p��4�����7�����Ԍ BIG CPU, ... Concepts of Parallel and Distributed Systems • CSCI 251-02 • Fall Semester 2018 Course Page: Alan Kaminsky • Hide most or all of the process creation and network communication, Multicore cluster (hybrid) parallel programs, Multiple threads per process, one thread per core, Shared memory parallel programming within each node, Message passing parallel programming between nodes, Getting OpenMP and MPI to work together is fraught with difficulties, CPU copies input data from CPU memory to GPU memory, Each thread takes input data from GPU memory, computes results, stores results back in GPU memory, CPU copies output data from GPU memory to CPU memory, Supports CPU main programs written in Java with GPU kernel functions written in CUDA, 1 flops = 1 floating point operation per second, Measures processing speed on the LINPACK linear algebra benchmark, Solve the matrix equation Ax = b for a very large dense matrix.

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