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MULTICORE, PARALLELISM, AND MULTITHREADING By: Eric Boren, Charles Noneman, and Kristen Janick MULTICORE PROCESSING Why we care What is it? A processor with more than one core on a single chip Core: An independent system capable of processing instructions and modifying registers and memory Motivation Advancements in component technology and optimization are limited in contribution to processor speed Many CPU applications attempt to do multiple things at once: Video editing Multi-agent simulation So, use multiple cores to get it done faster Hurdles Instruction assignment (who does what?) Mostly delegated to the operating system Can be done to a small degree through dependency analysis on the chip Cores must still communicate at times – how? Shared-memory Message passing Advantages Multiple Programs: Can be separated between cores Other programs don’t suffer when one hogs CPU Multi-threaded Applications: Independent threads don’t have to wait as long for each other – results in faster overall execution VS Multiple Processors Less distance between chips - faster communication results in higher maximum clock rate Less expensive due to smaller overall chip area, shared components (caches, etc.) Disadvantages OS and programs must be optimized for multiple cores, or no gain will be seen In a singly-threaded application, little to no improvement Overhead in assigning tasks to cores Real bottleneck is typically memory and disk access time – independent of number of cores Amdahl’s Law Potential performance increase on a parallel computing platform is given by Amdahl’s law. Large problems are made up of several parallelizable parts and non-parallelizable parts. S = 1/(1-P) S = speed-up of program P = fraction of program that is parallizable Current State of the Art Commercial processors: Most have at least 2 cores Quad-core are highly popular for desktop applications 6-core processors have recently appeared on the market (Intel’s i7 980X) 8-core exist but are less common Academic and research: MIT: RAW 16-core Intel Polaris – 80-core UC Davis: AsAP – 36 and 167-core, individually-clocked PARALLELISM What is Parallel Computing? Form of computation in which many calculations are carried out simultaneously. Operating on the principle that large problems can often be divided into smaller ones, which are solved concurrently. Types of Parallelism Bit level parallelism Instruction level parallelism Instructions combined into groups Data parallelism Increase processor word size Distribute data over different computing environments Task parallelism Distribute threads across different computing environments Flynn’s Taxonomy Single Instruction, Single Data (SISD) Provides no parallelism in hardware 1 data stream processed by the CPU in 1 clock cycle Instructions executed in serial fashion Multiple Instruction, Single Data (MISD) Process single data stream using multiple instruction streams simultaneously More theoretical model than practical model Single Instruction, Multiple Data (SIMD) Single instruction steam has ability to process multiple data streams in 1 clock cycle Takes operation specified in one instruction and applies it to more than 1 set of data elements at 1 time Suitable for graphics and image processing Multiple Instruction, Multiple Data (MIMD) Different processors can execute different instructions on different pieces of data Each processor can run independent task Automatic parallelization The goal is to relieve programmers from the tedious and error-prone manual parallelization process. Parallelizing compiler tries to split up a loops so that its iterations can be executed on separate processors concurrently Identify dependences between references -independent actions can operate in parallel Parallel Programming languages Concurrent programming languages, libraries, API’s, and parallel programming models have been created for programming parallel computers. Parallel languages make it easier to write parallel algorithms Resulting code will run more efficiently because the compiler will have more information to work with Easier to identify data dependencies so that the runtime system can implicitly schedule independent work MULTITHREADING TECHNIQUES fork() Make a (nearly) exact duplicate of the process Good when there is no or almost no need to communicate between processes Often used for servers fork() Parent Globals Heap Stack Child Globals Heap Stack Child Globals Heap Stack Child Globals Heap Stack Child Globals Heap Stack fork() pid_t pID = fork(); if (pID == 0) { //child } else { //parent } POSIX Threads C library for threading Available in Linux, OS X Shared Memory Threads are created and destroyed manually Has mechanisms for locking memory POSIX Threads Process Globals Heap Thread Thread Thread Thread Stack Stack Stack Stack POSIX Threads pthread_t thread; pthread_create( &thread, NULL, function_to_call, (void*) data); //Do stuff pthread_join(thread, NULL); POSIX Threads int total = 0; void do_work() { //Do stuff to create “result” total = total + result; } Thread 1 reads total (0) Thread 2 reads total (0) Thread 1 does add and saves total (1) Thread 2 does add and saves total (2) POSIX Threads int total = 0; pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER; void do_work() { //Do stuff to create “result” pthread_mutex_lock( &mutex ); total = total + result; pthread_mutex_unlock( &mutex ); } OpenMP Library and compiler directives for multi-threading Support in Visual C++, gcc Code compiles even if compiler doesn't support OpenMP Popular in high performance communities Easy to add parallelism to existing code OpenMP Initialize an Array const int array_size = 100000; int i, a[array_size]; #pragma omp parallel for for (i = 0; i < array_size; i++) { a[i] = 2 * i; } OpenMP Reduction #pragma omp parallel for reduction(+:total) for(i = 0; i < array_size; i++) { total = total + a[i]; } Grand Central Dispatch Apple Technology for Multi-Threading Programmer puts work into queues A system central process determines the number threads to give to each queue Add code to queues using a closure Right now Mac only, but open source Easy to add parallelism to existing code Grand Central Dispatch Initialize an Array dispatch_apply(array_size, dispatch_get_global_queue(0, 0), ^(int i) { a[i] = 2*i; }); Grand Central Dispatch GUI Example void analyzeDocument(doc) { do_analysis(doc); //May take a very long time update_display(); } Grand Central Dispatch GUI Example void analyzeDocument(doc) { dispatch_async(dispatch_get_global_queue(0, 0), ^{ do_analysis(doc); update_display(); }); } Other Technologies Threading in Java, Python, etc. MPI – for clusters QUESTIONS? Supplemental Reading Introduction to Parallel Computing https://computing.llnl.gov/tutorials/parallel_comp/#A bstract Introduction to Multi-Core Architecture http://www.intel.com/intelpress/samples/mcp_samplec h01.pdf CPU History: A timeline of microprocessors http://everything2.com/title/CPU+history%253A+A+t imeline+of+microprocessors