Suggested preparation: Strong programming skills, algorithms and computer architecture basics.
Optional preparation: Free MIT Course: Multicore and Parallel Programming.
About this course
Learn to reason about software performance across algorithms, memory and parallel execution. MIT lectures examine matrix operations, multicore programming, allocation and the challenges of nondeterministic computation.
Topics and lesson examples
- 1. Introduction and Matrix Multiplication
- 6. Multicore Programming
- 12. Parallel Storage Allocation
- 17. Synchronization Without Locks
- 23. High Performance in Dynamic Languages
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Professional — WikiFree study-level assessment |
| Language | English |
| Available lessons | 23 |
| Video duration | 29h 47m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Strong programming skills, algorithms and computer architecture basics.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
Related courses for your next step
- Review a lower-level optionFree MIT Course: Multicore and Parallel Programming
Focus: Lecture 1: Course Introduction (part 2) · R6: SIMD Programming on Cell
Intermediate - Review a lower-level optionFree Simplilearn Course: Software Development and Programming Foundations
Focus: Java & C++ · JavaScript
Beginner - Compare another approachFree MIT Course: Compiler Design and Computer Language Engineering
Focus: MIT Course: Compiler Design and Computer Language Engineering
Professional
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Software Performance Engineering | Professional | 1. Introduction and Matrix Multiplication · 6. Multicore Programming · 12. Parallel Storage Allocation | 29h 47m of video; practice time is additional |
| Free MIT Course: Multicore and Parallel Programming | Intermediate | Lecture 1: Course Introduction (part 2) · R6: SIMD Programming on Cell · Project: Battery simulation | 19h 44m of video; practice time is additional |
| Free Simplilearn Course: Software Development and Programming Foundations | Beginner | Java & C++ · JavaScript · Software Design | 234h 1m of video; practice time is additional |
| Free MIT Course: Compiler Design and Computer Language Engineering | Professional | MIT Course: Compiler Design and Computer Language Engineering | 6h 40m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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