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Curricular information is subject to change
By the end of this module, students should: Understand the main principles of parallel computing and orient themselves in parallel computing technologies; Be able to apply simple performance models to performance analysis of parallel algorithms; Be able to write parallel programs using MPI, OpenMP and Pthreads, and MPI+OpenMP; Be able to to theoretically and experimentally analyse the performance of parallel applications.Indicative Module Content:
Vector and superscalar processors: architecture and programming model, optimizing compilers (dependency analysis and code generation), array libraries (BLAS), parallel languages (Fortran 90).
Shared-memory multi-processors and multicore CPUs: architecture and programming models, optimizing compilers, thread libraries (Pthreads), parallel languages (OpenMP).
Distributed-memory multi-processors: architecture and programming model, performance models, message-passing libraries (MPI), parallel languages (HPF).
Hybrid parallel programming for clusters of mutlicore CPUs with MPI+OpenMP.
|Student Effort Type||Hours|
|Autonomous Student Learning||
Not applicable to this module.
|Description||Timing||Component Scale||% of Final Grade|
|Practical Examination: Lab. assignments||Varies over the Trimester||n/a||Standard conversion grade scale 40%||No||
|Examination: Final examination||2 hour End of Trimester Exam||No||Standard conversion grade scale 40%||No||
|Remediation Type||Remediation Timing|
|In-Module Resit||Prior to relevant Programme Exam Board|
• Feedback individually to students, post-assessment
• Online automated feedback
Assignments' grades are released with comments from TA. Student can ask TA for further individual feedback regarding assessments.