Wheeler, Travis J

English

University of Montana

University of MontanaScholarWorks at University of MontanaSyllabi Course SyllabiSpring 2-1-2019CSCI 580.01: Applied Parallel ComputingTechniquesTravis J. WheelerUniversity of Montana, travis.wheeler@umontana.eduLet us know how access to this document benefits you.Follow this and additional works at: https://scholarworks.umt.edu/syllabiThis Syllabus is brought to you for free and open access by the Course Syllabi at ScholarWorks at University of Montana. It has been accepted forinclusion in Syllabi by an authorized administrator of ScholarWorks at University of Montana. For more information, please contactscholarworks@mso.umt.edu.Recommended CitationWheeler, Travis J., "CSCI 580.01: Applied Parallel Computing Techniques" (2019). Syllabi. 9402.https://scholarworks.umt.edu/syllabi/9402	 			 	 		 	 		 	 			 	 	 	 	 			 		 			 					 																													 	 	 	 	 				 		 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 		 		 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 			 		 	 			 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 		 	 	 	 	 		 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	CSCI	 480/580:	 Parallel	 Computing	 Course information Spring	2019Meeting time: T/Th	9:30-10:50AM Location:	 Social Science 362 Final Exam: none Course material/submissions/grades are in Moodle (http://umonline.umt.edu) Instructor information Instructor: Travis	Wheeler Office: 	Social	Science 420 E-mail: travis.wheeler@umontana.edu Phone:	406-243-6219 Office 	Hours: TBD or by appointment (see http://wheelerlab.org/schedule) Course Overview This course is an introduction to parallelism	 and parallel programming. Topics includethe various forms of parallelism	 on modern computer hardware (e.g. multiple	cores,	SIMD vector 	instructions,	GPUs,	 FPGAs, and networked	clusters),	with	coverage	of	 locality and 	latency,	shared 	vs 	non-shared memory, and synchronization mechanisms(locking, atomicity, etc). We will address computer architecture at a high level, sufficientto 	understand 	the 	relative 	costs 	of 	operations like arithmetic and data transfer. We 	will	 discuss patterns that appear in essentially all programs that need to run fast. The	 emphasis will be on implementing actual parallel applications, rather than mere theory. Course PrerequisitesCSCI 205	 and	 232, or	 instructor	 consent. Regarding instructor consent: this course may prove useful for students from	 manydepartments and with different backgrounds. I will assume mature programming skillsin	a 	conventional (non-parallel)	language,	as well as enough mathematical skills tounderstand the problems and algorithmic solutions presented. Course projects will beimplemented in C – I strongly recommend having familiarity with C prior to thebeginning of class, for example using this online tutorial: http://www.cprogramming.com/tutorial/c-tutorial.html. Course Learning	 outcomes •	 Able to recognize and analyze patterns/applications that benefit from	 parallelism •	 Able to implement basic parallel computing in multithreaded environment (OpenMP,	 Pthreads) •	 Able to implement basic parallel computing in GPU environment (CUDA) •	 Able to implement basic SIMD computing approaches  	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 	 	 	 						 	 	 							 			 		 	 	 	 	 									 							 						 						 							 						 									 	 			 	 			 						 	 			 	 		 				 	 	 	 	 	 								•	 Able to implement basic parallel computing in a cluster environment (MPI) •	 Able to analyze and measure performance of parallel computing systems, and todebug those systems •	 Able to analyze the impact of latency and resource contention on throughput Additionally, for graduate students: •	 Experience presenting complex computer science (algorithm) methods and results to an	audience Required	textbook 	Parallel Computing for	 Science	 and Engineering,	by	 Victor	 Eijkhouthttps://moodle.umt.edu/pluginfile.php/1579508/mod_resource/content/1/EijkhoutParComp.pdf Other resources An open source textbook by Norm	 Matloff:http://heather.cs.ucdavis.edu/~matloff/158/PLN/ParProcBook.pdfOpenMPhttps://www.youtube.com/playlist?list=PLLX-Q6B8xqZ8n8bwjGdzBJ25X2utwnoEG http://openmp.org/mp-documents/Intro_To_OpenMP_Mattson.pdf http://openmp.org/mp-documents/Mattson_OMP_exercises.zip CUDA https://www.udacity.com/course/intro-to-parallel-programming--cs344 https://github.com/udacity/cs344 Grading		UndergraduateHomework: 40% Exams: 30% In-class	activities 30% Graduate Homework: 30% Exams: 20% In-class	activities	 25% Work/Presentations		25% Grade	cutoffs: curved,	 based on my opinion of the 	work	of 	students 	at	the 	boundary. 	 		 	 	 		 	 		 			 		 	 							 		 		 	 	 		 	 	 		 	 	 		 	 	 		 	 		 	 		 	 		 	 		 	 		 	 				 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 				 	 	 	 	 	 	 	 	 	 	 	 	 						 		 	 	 	 	 	 	 	 	 	 		 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		Approximate schedule (find	updated reading	 and assignments	 in 	moodle) Week Content Reading	/	Videos 1 Intro OpenMP OpenMP	videos 1-6 2 OpenMP OpenMP	videos 	7-17 3 OpenMP OpenMP	videos 	18-27 4 GPU CUDA	 videos	lesson	1 5 GPU/CUDA CUDA	 videos lesson	2,3 6 GPU/CUDA CUDA	 videos lesson	4 7 GPU/CUDA CUDA	 videos lesson	5 8 GPU/CUDA CUDA	 videos lesson	6 9 SIMD /	SSE 10 MPI 11 MPI 12 FPGA 13 FPGA 15 Presentations Working	 in groups	 (homework)The best way to learn the material is by solving problems. You are encouraged to worktogether - the best way to understand the subtleties of the homework problems is toargue about the answers. Each of you should look at all the problems independently, and	 not just divide the list in two parts each time. After discussing problems and coming up with 	solutions,	you	will 	each	 write up a separate submission. Though the ideas behindyour solutions may be quite similar, the text should not be identical – demonstrate	 your	command of the problem	 with a personalized solution. I retain the right to question youabout the material turned in. If it is evident that you don’t understand what you turned in,	your	grade	will 	be	lowered. (Don’t 	be	a 	leech	and	let 	your	partner	do	all 	the	work.	Unless	you	learn	how 	to	solve	 problems, you will get burned on the exams and thus for your final grade.) Working	 in groups	 (programming	 assignments)I encourage discussion with others regarding programming assignments, as well. As with homework, these should be high-level	discussions.	Code 	should be 	written	 independently. If I suspect copying or plagiarism, I will ask you to explain each piece ofthe code to me, possibly resulting in a reduced grade or removal from	 class. 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 	 	 		 		 					 	 	 		 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 	 	 	 	 	 	 	 	 		 	 	 	 	 		 	 	 	 	 	 	 			 	 	 	 	 	 	 	 	 	 	 		 	 			 	 	 	 	 	 	 	 	 		 		 	 	 	 	 	 	 	 	 	 		 	 	Late	policy 	Submissions for programming and homework assignments are due at the beginning ofclass. Late submissions will not be accepted. Every student will get one free extension onan assignment (programming or homework) for up to a week. You do not have to ask for	 this – just	write	that	you	are	using	your 	free	extension	when	you	turn	it	in.	Don’t	waste	 this 	extension	or 	feel	obligated to 	use 	it; 	another 	extension	will	be 	given	only 	in	 exceptional circumstances. Cheating	It	should 	go	without	saying	that	academic dishonesty (including plagiarism	 andcheating) will not be tolerated. Consult the university’s student conduct code for more details. I will follow the guidelines given there. I will seek out the maximum	 allowablepenalty for any academic dishonesty that	occurs 	in	this 	course.	If 	you	have 	questions about which behaviors are acceptable, please ask me. Disabilities Students with disabilities are encouraged to meet with me to discuss anyaccommodations they require. Electronic	 devices Turn	off	your	 cellphone,	or	set it 	to	vibrate	during	class.	Take	calls	outside	the	classroom. Students texting during class will be asked to leave. Personal contact I	 like to 	establish 	personal	contact	 in my classes. Please feel welcome to stop by my office (during	office	hours	or	otherwise) to 	ask	questions 	or 	say 	hello.	 

CSCI 580.01: Applied Parallel Computing Techniques

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CSCI 580.01: Applied Parallel Computing Techniques

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