San Jose State University

CS 159:  Parallel Processing

Spring 2010 Course Syllabus



Description:       A combination hardware architecture and software development class focused on multi-threaded, parallel processing algorithms and techniques.  Overview of high-performance parallel processing hardware architectures ranging from on-chip Instruction-Level Parallelism to multi-core microprocessor chips to large distributed supercomputing systems including Clusters, Grids, and Clouds.  Discussion and hands-on exercises in a broad range of various parallel programming paradigms and languages such as Pthreads, MPI, OpenMP, Map-Reduce Hadoop, CUDA and OpenCL.  The class focus will be on understanding the fundamental concepts associated with the design and analysis of parallel processing systems.  Special emphasis will be placed on avoiding the unique non-deterministic software defects that can arise in parallel processing systems including race conditions and deadlocks.  The class will also provide overview of current parallel software development toolkits including debuggers and performance profilers.



Meeting Time:    Section 1:  MW  1900-2015  MH223


Prerequisites:     CS 146 (CS 147 and CS 149 highly recommended), or instructor consent.

Instructor:  Robert K. Chun

Contact Info:       EMAIL:,  PHONE: (408) 924-5137,  OFFICE: MH 413

Office Hours:     MW 15:45 – 17:00 and MW 20:15 – 21:30


Textbooks: Required:     Multi-Core Programming, Shameem Akhter and Jason Roberts, 2006, Intel Press, ISBN 0-9764832-4-6


                                       Required:                                                                                                       Using OpenMP, Barbara Chapman and Gabriele Jost, 2008, MIT Press, ISBN 978-0-262-53302-7.


                                       Optional:                                                                                                       Scientific Parallel Computing, Ridgway Scott and Terry Clark, 2005, Princeton University Press, ISBN 0-691-11935-X




Grading:    Grading consists of two midterms, one final, and a set of projects (consisting of a combination of written problems and programming assignments) weighted as follows.  Grading is based on a class curve.  All assignments must be completed by the student on the due date specified to receive credit for the class.  Late assignments or exams are not accepted.  All students must uphold academic integrity per university policy detailed at


                              15%     Midterm Exam 1                               Week 6 (Approximate)                      

                              15%     Midterm Exam 2                               Week 12 (Approximate)

                              40%     HW and Programming Projects        Due as announced in class                          

                              30%     Final Exam                                       5/19/10 at 19:45-22:00

Student Learning Outcomes:


  Upon successful completion of this course, students should be able to understand:


·       The Technical and Business motivation and need for current state-of-the-art computing systems to incorporate Parallel Processing into the Hardware and Software Subsystems.

·       The Micro-Hardware Architectural Evolutionary Trends leading to on-chip Instruction-Level Parallelism, and Pipelining, SuperScalar, Multi-Function Unit Parallel Processing.

·       The Macro-Hardware Architectural Evolutionary Trends leading to Parallel Processing including Flynn’s Taxonomy and the recent progression in high-performance supercomputing architectures from Clusters to Grids and to Clouds.

·       Data dependency analysis and hazards which, along with Amdahl’s Law, limits the amount of practical speedup and scalability that can be achieved with Parallel Processing.

·       Design and Analysis Techniques for Parallel Processing Systems including the identification of data vs. task partitioning in algorithms and applications.

·       The Different Models for implementing parallelism in Computing Systems such as shared memory and message passing.

·       The software challenges associated with Parallel Processing including the difference between concurrent vs. parallel execution models, deadlocks and race conditions.

·       A sample of current parallel programming paradigms and languages and be able to write parallel programs using them.



Schedule (Tentative):








1 - 3                                         Introduction, Motivation and Overview of Parallel Processing with

                                                an emphasis on the Micro- and Macro-Hardware Evolutionary Trends

leading to Parallelism and the Software Challenges of Parallelism


4 - 6                             Hardware Parallel Processing including pipelining and Instruction-Level

                                    Parallelism (ILP)


7 - 8                             Multi-Function Parallelism in Hardware


9                                  Data dependency analysis and control hazard analysis including RAW,

                                    WAR, WAW, and Branch Prediction


10                                Limitations of Hardware-based, Software-transparent ILP


11 - 17                         Software Challenges of Parallel Processing including Concurrent vs. Parallel

                                                Execution Models, Amdahl’s Law, Deadlocks, Race Conditions, Semaphores


18                                            Models of Parallelism such as Shared Memory, Message Passing


19 - 25                         Parallel Programming Paradigms including Unix Process Forking, PVM,

MPI, OpenMP, CUDA, OpenCL, Hadoop Map-Reduce.


26                                            GPGPU


27                                            Toolsets for Parallel Program Software Development and Debugging



General University Policies



If you need course adaptations or accommodations because of a disability, or if you need special arrangements in case the building must be evacuated, please inform the instructor as soon as possible.  Presidential Directive 97-03 requires that students with disabilities register with DRC to establish a record of their disability.



Academic integrity is essential to the mission of San José State University.  As such, students are expected to perform their own work (except when collaboration is expressly permitted by the course instructor) without the use of any outside resources.  Students are not permitted to use old tests or quizzes when preparing for exams, nor may they consult with students who have already taken the exam. When practiced, academic integrity ensures that all students are fairly graded.


We all share the obligation to maintain an environment which practices academic integrity.  Violations to the Academic Integrity Policy undermine the educational process and will not be tolerated.  It also demonstrates a lack of respect for oneself, fellow students and the course instructor, and can ruin the university’s reputation and the value of the degrees it offers. Violators of the Academic Integrity Policy will be subject to failing this course and being reported to the Office of Judicial Affairs for disciplinary action which could result in suspension or expulsion from San José State University.



At SJSU, cheating is the act of obtaining or attempting to obtain credit for academic work through the use of any dishonest, deceptive, or fraudulent means. Cheating at SJSU includes but is not limited to:


Copying in part or in whole, from another’s test or other evaluation instrument; Submitting work previously graded in another course unless this has been approved by the course instructor or by departmental policy. Submitting work simultaneously presented in two courses, unless this has been approved by both course instructors or by departmental policy.  Altering or interfering with grading or grading instructions; Sitting for an examination by a surrogate, or as a surrogate; any other act committed by a student in the course of his or her academic work which defrauds or misrepresents, including aiding or abetting in any of the actions defined above.



At SJSU plagiarism is the act of representing the work of another as one’s own (without giving appropriate credit) regardless of how that work was obtained, and submitting it to fulfill academic requirements. Plagiarism at SJSU includes but is not limited to:


The act of incorporating the ideas, words, sentences, paragraphs, or parts thereof, or the specific substances of another’s work, without giving appropriate credit, and representing the product as one’s own work; and representing another’s artistic/scholarly works such as musical compositions, computer programs, photographs, painting, drawing, sculptures, or similar works as one’s own.


Additional Information: