Fundamentals of parallel programming and algorithm design to speedup machine learning and scientific computation. Topics include parallel architectures with shared memory machines and clusters, GPUs, message-passing and thread programming, parallel performance evaluation and optimization, parallelization of core matrix algorithms in machine learning and scientific computing applications, and MapReduce programming for data-intensive cloud computing.
4
UnitsLetter
Grading1, 2, 3
PasstimeNone
Level LimitEngineering
CollegeLectures and slides were hard to follow so nobody attended. Midterm / final were open computer but were still pretty hard as they had lots of tricky MCQs. He curved the overall grade pretty generously. Overall, not a fan of his lectures but would take the class for the content.
Awesome class
Nice guy, his accent is a little hard to understand sometimes though. His assignments are generally not that bad and the material he covers is pretty interesting. Not the best lecturer but he's very reasonable and responsive.
legend
Aside from heavy accents and projects that are a little hard, everything seems fine for me. The material covered was interesting and the professor is a nice person
Useless, incoherent lectures. Obviously does not have any passion for teaching students. You will have to self-study all the material. Tests and homework are relatively easy, but programming assignments are long and frustrating. Felt like a waste of a quarter; disappointing, because the material should be interesting.
Subhash Suri
4.6
Ambuj Singh
2.4
Ben Hardekopf
4.7
Yekaterina Kate Kharitonova
2.5
Jonathan Balkind
3.6
Yu Feng
2.7