Your 15-418 final project gives you the opportunity to dive deeply into a fun parallel systems problem of your choosing. One way to do this is to design a parallel solution to a problem in an application area that is interesting to you. Projects you have done in other classes are a good source of ideas. For example, projects in machine learning, AI, graphics, computational photography, and computer vision often stand to benefit greatly from parallelization.

Other project ideas focus on system design or workload evaluation. For example, a project might compare the performance of CPU and GPU implementations of a similar problem, and describe which platform is better suited for the job. You could simulate the behavior of code on machines with different SIMD widths, add a feature to the ISPC compiler (its implementation is open source), or develop a parallel debugging tool that helps visualize bottlenecks and performance in parallel programs.

Create a web page for your project. The purpose of the proposal is two-fold: (1) it forces you to organize your thoughts about your project (2) it gives 15-418 course staff the ability to verify your plans are of the right scope given our expectations (it also gives us the ability to offer suggestions and help).

A template just your project web page is provided here. You do not need to use the template your your project page should contain the same sections and same content.

Your proposal should include the following parts:

1. SUMMARY. Summarize your project in a few sentences. Describe what you plan to do and what parallel systems you will be working with. Example one-liners include (you should add a bit more detail):
   • We are going to implement an optimized Smoothed Particle Hydrodynamics fluid solver on the NVIDIA GPUs in the lab.
   • We are going port the Go runtime to Blacklight.
   • We are going to create optimized implementations of sparse-matrix multiplication on both GPU and multi-core CPU platforms, and perform a detailed analysis of both systems' performance characteristics.
   • We are going to back-engineer the unpublished machine specifications of the GPU in the tablet my partner just purchased.
2. BACKGROUND. If your project involves accelerating a compute-intensive application, describe the application or piece of the application you are going to implement in more detail. This description should be 1-2 paragraphs. It might be helpful to include a block diagram or pseudocode of the basic idea. What aspects of the problem might benefit from parallelism?
3. THE CHALLENGE. Describe why the problem is challenging. What aspects of the problem might make it difficult to parallelize?
   • Describe the workload: what are the dependencies, what are its memory access characteristics? (is there locality? is there a high communication to computation ratio?), is there divergent execution.
   • Describe constraints: What are the properties of the system that make mapping the workload to it challenging.
4. RESOURCES. Describe the resources you will use. What code base will you start from? Are you starting from scratch or using an existing piece of code? Are there any other resources you need, but haven't figured out how to obtain yet?
5. GOALS/DELIVERABLES. Describe the deliverables or goals of your project.
   Separate your goals into what you PLAN TO ACHIEVE (what you believe you must get done to have a successful project) and an extra goal or two that you HOPE TO ACHIEVE if the project goes really well and you get ahead of schedule. It may not be possible to state precise performance goals at this time, but we encourage you be as precise as possible. If you do state a goal, give some justification of why you think you can achieve it.
   • If applicable, describe the demo you plan to show at the parallelism computation (will it be an interactive demo on a certain problem size, will you show an output of the program that is really neat, will you show speedup graphs reaching a certain performance level?)
   • If your project is an analysis project, what are you hoping to learn about the workload or system being studied? What question(s) do you plan to answer?
   • Systems project proposals should describe what the system will be capable of and what performance is hoped to be achieved.
6. PLATFORM CHOICE. Describe why the platform (computer and/or language) you have chosen is a good one for your needs. Why does it make sense to use this parallel system for the workload you have chosen?
7. SCHEDULE. Produce a schedule for your project by filling out the following table. List what you plan to get done each week from now until May 10th in order to meet your project goals. Keep in mind that due to other classes, you'll have more time to work some weeks than others (work that into the schedule). You will need to re-evaluate your progress at the end of each week and update this schedule accordingly. Note the intermediate checkpoint deadline is April 20th. In your schedule we encourage you to be precise as precise as possible. It's often helpful to work backward from your deliverables and goals, writing down all the little things you'll need to do (establish the dependencies!).

   Week	What We Plan To Do	What We Actually Did
   Apr 1-7
   Apr 8-14
   Apr 15-21
   Apr 22-28
   Apr 29-May 5
   May 6-11

The following are suggestions for information to include in your checkpoint write-up. Your goal in the writeup is to assure the course staff that your project is proceeding as you said it would in your proposal. If it is not, your checkpoint writeup should emphasize what has been causing you problems, and provide an adjust schedule and goals. As projects differ, not all items in the list below are relevant to all projects.

• Make sure your project schedule on your main project page is up to date with work completed so far, and well as with a revised plan of work for the coming weeks. As by this time you should have a good understanding of what is required to complete your project, I want to see a very detailed schedule for the coming weeks. I suggest breaking time down into half-week increments. Each increment should have multiple tasks, and for each task put a person's name on it. An example skeleton schedule is below.
• In a couple of paragraphs, summarize the work that you have completed so far. (This should be easy if you have been maintaining this information on your project page.)
• Describe how you are doing with respect to the goals and deliverables stated in your proposal. Do you still believe you will be able to produce all your deliverables? If not, why? What about the "nice to haves"? In your checkpoint writeup we want a new list of goals that you plan to hit for May 10th.
• What do you plan to show at the parallelism competition? Will it be a demo? Will it be a graph?
• Do you have preliminary results at this time? If so, it would be great to included them in your checkpoint write-up.
• List the issues that concern you the most. Are there any remaining unknowns (things you simply don't know how to solve, or resource you don't know how to get) or is it just a matter of coding and doing the work? If you do not wish to put this information on a public web site you are welcome to email the staff directly.
• [Optionally] schedule a meeting with the course staff to discuss progress

Example Checkpoint 2 Schedule

Week	What We Plan To Do	What We Actually Did
Mon 4/23-Thu 4/26
Fri 4/27-Sun 4/29
Mon 4/30-Thu 5/3
Fri 5/4-Sun 5/6
Mon 5/7-Thu 5/10

Suggestions and expectations for project presentations were discussed in class. Please see the posted slides from Lecture 26.

Your proposal should include the following basic sections. Not all the sub-bullets apply to all projects, but they are given as examples/suggestions of issues to address. You are also encouraged to provide more detail if you wish. Note that some of the information in your final writeup can be pulled directly from your proposal if it is still accurate.

1. SUMMARY. A two to three sentence project summary. The summary should list your project deliverables and what machines they ran on.
   • Example: We implemented smooth particle hydrodynamics in CUDA on the GPU and in ISPC on the CPU and compared the performance of the two implementations.
   • Example: We parallelized a chess bot. Our 64 core implementation on Blacklight achieves a 40x speedup and won several games on an internet chess server.
   • Example: We accelerated image processing operations using the GPU. Given the speed of our implementation, we demonstrate that a brute-force approach to breaking CAPTCHAS is effective.
2. BACKGROUND. Describe the algorithm, application, or system you parallelized in computer science terms. (Recall our discussion from class.) A figure would be really useful here.
   • What are the key data structures?
   • What are the key operations on these data structures?
   • What are the algorithm's inputs and outputs?
   • What is the part that computationally expensive and could benefit from parallelization?
   • Break down the workload. Where are the dependencies in the program? How much parallelism is there? Is it data-parallel? Where is the locality? Is it amenable to SIMD execution?
3. APPROACH. Tell us how your implementation works. Your description should be sufficiently detailed to provide the course staff a basic understanding of your approach. Again, it might be very useful to include a figure here illustrating components of the system and/or their mapping to parallel hardware.
   • Describe the technologies used. What language/APIs? What machines did you target?
   • Describe how you mapped the problem to your target parallel machine(s). IMPORTANT: How do the data structures and operations you described in part 2 map to machine concepts like cores and threads. (or warps, thread blocks, gangs, etc.)
   • Did you change the original serial algorithm to enable better mapping to a parallel machine?
   • If your project involved many iterations of optimization, please describe this process as well. What did you try that did not work? How did you arrive at your solution? The notes you've been writing throughout your project should be helpful here. Convince us you worked hard to arrive at a good solution.
   • If you started with an existing piece of code, please mention it (and where it came from) here.
4. RESULTS. How successful were you at achieving your goals? We expect results sections to differ from project to project, but we expect your evaluation to be very thorough (your project evaluation is a great way to demonstrate you understood topics from this course). Here are a few ideas:
• If your project was optimizing an algorithm, please define how you measured performance. Is it wall-clock time? Speedup? An application specific rate? (e.g., moves per second, images/sec)
• Please also describe your experimental setup. What were the size of the inputs? How were requests generated?
• Provide graphs of speed up. Please precisely define the configurations being compared. Is your baseline single-threaded CPU code? It is an optimized parallel implementation for a single CPU?
• Recall the importance of problem size. Is it important to report results for different problem sizes for your project? Do different workloads exhibit different execution behavior?
• IMPORTANT: What limited your speedup? Is it a lack of parallelism? (dependencies) Communication or synchronization overhead? Data transfer (memory-bound or bus transfer bound). Poor SIMD utilization due to divergence? As you try and answer these questions, we strongly prefer that you provide data and measurements to support your conclusions. If you are merely speculating, please state this explicitly. Performing a solid analysis of your implementation is a good way to pick up credit even if your optimization efforts did not yield the performance you were hoping for.
• Deeper analysis: Can you break execution time of your algorithm into a number of distinct components. What percentage of time is spent in each region? Where is there room to improve?
• Was your choice of machine target sound? (If you chose a GPU, would a CPU have been a better choice? Or vice versa.)
5. REFERENCES. Please provide a list of references used in the project.
6. LIST OF WORK BY EACH STUDENT. If your project is a team project, please list the work performed by each partner. If you do not feel comfortable placing this information on a public web page, you may email the course staff this information directly.

• Game playing: Chess, Go, etc.
• Graphics: extend assignment 2 to achieve higher performance under real workloads (render real triangle meshes and more complex shading functions); implement a parallel ray tracer
• Physical simulation: fluid simulation, rigid body solver, cloth simulation (for those that have taken Prof. Treuille's class)
• Annotate the compiled ISPC code with calls to CPU performance monitor instructions and then gather interesting statistics about program execution: cache hits/misses, IPC (and scalar IPC and vector IPC separately). Create a visualization tool for the results. (from Matt Pharr)
• For the really brave: Add polymorphic functions to ISPC: implement a function template mechanism in the compiler since it gets to be painful to write multiple versions of functions with both uniform and varying parameter types. (from Matt Pharr).
• Image processing (for those that have taken Efros' computational photography class)
• Vision: detect features then train an SVM classifier used for high-quality image search. (note this is a general description but there's a specific algorithm vision researchers here at CMU have in mind, they'd love to have someone try to accelerate it... starter code is available)
• Vision: image/video tagging (cast as an integer programming problem). Another research project looking for a faster solution. MATLAB starter code available.
• Study a workload's amenability to SIMD execution. Simulate behavior given multiple SIMD widths.
• Implement a sparse matrix equation solver.

• Spring 2011
• Spring 2008
• Spring 2004