1. Adding PDC within a Six-Course Subset of the CS Major Apan Qasem Texas State University

2. Context Texas State University Current enrollment ~35000 30,000 undergrad, 5,000 graduate Student body reflects area demographic 28% of students are of Hispanic origin HSI designation since 2011 CS Department Faculty 21 TTF + 6 Lecturers 7-8 “rotating” adjuncts every semester Students ~650 declared CS majors ~90 graduate students Class sizes relatively small (< 40) SIGCSE14Texas State University 2

3. The Early-and-Often Approach Breaks away from the traditional approach of teaching parallel programming as an upper-level elective Follows the CSinParallel model (Brown, Shoop, Adams et al. ) Introduce parallelism early in the curriculum in required courses Repeat key concepts in different courses throughout the curriculum Tie concepts together in an upper- level capstone course SIGCSE14 upper-level elective major path capstone major path early introduction reinforce ment collation Texas State University 3

4. indicates prerequisite Indicates new course [Texas State CS curriculum] Data and Task Parallelism Divide-and-conquer Dynamic Programming B. Parallelization Techniques and Parallel Algorithms Communication Synchronization Scheduling for Power and Performance Data Dependence D. Task Orchestration SMP, Clusters NUMA, UMA Cache Sharing Cache Coherence C. Parallel Architectures Speedup Efficiency Scalability Cache Locality Load Balancing Complexity Analysis E. Performance Concurrency and Parallelism, Decomposition Power and Performance A. Elementary Notions low high Level of Abstraction CS II A,B,E CS I A Data Structures A,B, E Computer Architecture C,D,E Computer Networks B,D Operating Systems D,E Compilers D,E Graphics B, C Programming for Mutlicore and Manycore Platforms A,B,C,D,E freshman senior Year SIGCSE14Texas State University 4

5. Module Implementations to Date NoModule B1Parallelization techniques C1Intra-processor parallel architecture C2Inter-processor parallel architecture D1Task orchestration – synchronization and communication D2Task orchestration - scheduling and mapping E1Performance - basic concepts SIGCSE14 7 of 11 KUs from Tier 1 8 of 16 KUs from Tier 2 8 of 21 KUs from Elective 19 of 41 KUs total CS 2013 Coverage Adding two more in Fall 14 Texas State University 5

6. Parallelization Techniques Description Data and task parallelization techniques Data parallel programs with OpenMP CS2013 Mapping Recommended Courses CS II, Data Structures, Algorithms SIGCSE14 KAKnowledge UnitLevel SFComputational ParadigmsTier 1 SFParallelismTier 1 PDParallel DecompositionTier 1, Tier 2 PDParallel PerformanceTier 2 PDProcessing Elective PLConcurrency and Parallelism Texas State University 6

7. Intra-processor Parallel Architecture Description Instruction-level parallelism, superscalar, out-of-order execution Vector instructions, SMT Multicore memory hierarchy CS 2013 Mapping Recommended Courses Computer Organization, Computer Architecture SIGCSE14 KAKnowledge UnitLevel ARAssembly level machine organizationTier 2 ARFunctional organizationElective Texas State University 7

8. Inter-processor Parallel Architecture Description Shared-memory multiprocessors, distributed memory systems GPUS, SMTs, SSEs CS 2013 Mapping Recommended Courses Computer Organization, Computer Architecture SIGCSE14 KAKnowledge UnitLevel PDParallel Architecture Tier 1, Tier 2, Elective ARMultiprocessing and alternative architectures Elective Texas State University 8

9. Task Orchestration : Synchronization and Communication Description Point-to-point and collective communication Semaphores, locks, critical sections MPI and OpenMP CS 2013 Mapping Recommended Courses Operating Systems, Computer Networks SIGCSE14 KAKnowledge UnitLevel PDParallelism FundamentalsTier 1 PDCommunication and Coordination OSOperating System PrinciplesTier 2 OSConcurrency ARInterfacing and communication HCCollaboration and communicationElective Texas State University 9

10. Task Orchestration : Scheduling and Mapping Description Pipelined parallelism, producer-consumer applications Load balancing, energy efficiency, thread affinity SMT and hardware threads CS 2013 Mapping Recommended Courses Operating Systems SIGCSE14 KAKnowledge UnitLevel OSOperating System PrinciplesTier 2 OSConcurrency PDParallel Algorithms, Analysis, and ProgrammingTier 2, Elective Texas State University 10

11. Performance : Basic Concepts Description Amdahl’s law, linear and superlinear speedup, throughput, data locality, communication cost Study of parallel merge sort and matrix multiplication CS2013 Mapping Recommended Course CS I, Computer Architecture, Algorithms, Compilers SIGCSE14 KAKnowledge UnitLevel SFParallelismTier 1 SFEvaluation ARDigital logic and digital systemsTier 2 PDParallel PerformanceElective PLConcurrency and Parallelism Texas State University 11

12. Questions