The course is open for the entire academic year 2022–2023, specifically from 5.9.2022 to 31.8.2023. You can complete it on your own schedule and there are no other deadlines.
For technical reasons, the course is initially marked as ending at 31.12.2022 in SISU, but you can disregard this.
Join the course Discord channel via https://study.cs.helsinki.fi/discord/join/ppc. This is the primary avenue for asking for help and other discussion about the course.
Antti Laaksonen ([email protected]) is responsible for the course administration. Roope Salmi ([email protected]) serves as a teaching assistant and is responsible for the technical aspect of the course.
The course is designed to be completed in six intensive weeks, plus the prerequisite test. You may wish to complete the course at a slower pace. For instance, last year the University of Helsinki version of the course stretched each "week" into two calendar weeks.
Lecture material and recordings of lectures given by Jukka Suomela are available at https://ppc.cs.aalto.fi/.
Exercises are described and submitted at https://ppc-exercises.cs.aalto.fi/course/hy2022.
The coursework is divided into exercises and tasks. The tasks within each exercise focus on the same computational problem, but the intended techniques to solve them are different in each task.
Tasks are numbered according to the week they are intended to be worked on. It is not necessary to complete, much less get full points in all of them to achieve a good grade. However, weeks 1–5 have required tasks, each of which you will need to get at least one point in to get course credits.
Read more about the structure of the course below.
Read the instructions at https://ppc-exercises.cs.aalto.fi/help carefully.
Some details in the instructions only apply to lecture courses with weekly deadlines. Specific to this course,
- There is less human feedback. Occasional spot chekcs may be made to confirm that you are following the instructions of each task correctly. Also check the course structure for additional clarifications to the task instructions.
- The option "This submission is ready for grading" is selected for you automatically. This means that your submission is visible to course staff and subject to manual review. If you do not want this for a specific submission, select "Please ignore this submission". Selecting this means you cannot get any points from the submission.
There is a new feature for testing and iterating on your code remotely, which you may want to utilize to get more accurate measurements or especially if you do not have an NVIDIA GPU with CUDA development tools installed.
If, for example, you used this command to test locally:
./grading test
then you can add the --remote flag to instead run the command remotely.
./grading test --remote
When trying this for the first time, you will be instructed to configure an API token. Obtain an API token at https://ppc-exercises.cs.aalto.fi/token, and paste it into a configuration file in one of the suggested locations.
Your code and the specified tests are then sent to our grading servers, where they are run on the same hardware as normal submissions are. The output should otherwise be the same as when running locally, but it is only shown when the whole command is completed, so be patient.
Be mindful of other users, as the resources are limited and shared with normal submissions.
Grading is described at the end of the course page, https://ppc-exercises.cs.aalto.fi/course/hy2022. The minimum amount of points for course credits is 36. Additionally, you must have at least one point from every required task, i.e. CP1, MF2, CP3a, SO4 and CP5.
Once you are satisfied with the points and grade you have achieved, contact Antti Laaksonen ([email protected]) so we can register your credits in SISU. We may still review some of your submissions at this point, but we will only deduct points for major issues. You will also be able to go back and fix the issues if it affects your grade.
This week consists of the task Pre0, the prerequisite test worth one course point. It is not required, but some knowledge of C++ is assumed as prerequisite for the course. It is possible to complete the course without much prior knowledge in C++, but this will be more challenging and require additional self-study.
Introduction to the course and instruction-level parallelism.
Tasks this week: CP1, MF1
Required task: CP1
You are introduced to the exercises CP (correlated pairs), and MF (median filter). The tasks CP1 and MF1 have you implement baseline CPU solutions to these exercises, which are then improved upon using various techniques in further weeks.
Multicore parallelism with OpenMP and vector operations.
Tasks this week: CP2a, CP2b, CP2c, MF2
Required task: MF2
In tasks CP2abc, it is intended that you only use one technique in each task, and not e.g. all previous techniques combined. CP2a is about instruction-level parallelism, CP2b is about multicore parallelism, and CP2c is about vector operations. The techniques will be combined next week.
Combining vectorization, instruction-level and multicore parallelism. Reusing data in registers and cache.
Tasks this week: CP3a, CP3b
Required task: CP3a
It is intentionally very challenging to get full points from these tasks. You will need to apply your knowledge and experiment with different approaches.
Introduction to GPU and CUDA programming.
Tasks this week: CP4, IS4, SO4
Required task: SO4
GPU programming in depth.
Tasks this week: CP5, SO5
Required task: CP5
Assorted algorithmic techniques for designing high performance parallel programs. Concluding the course.
Tasks this week: IS6a, IS6b, SO6
There are also the additional tasks CP9a, IS9a and MF9a which you may want to do for further insight and course points.
For CP9a, you must use Strassen's algorithm. Submitting a solution from another CP task will not be accepted.
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