Your 15-418/15-618 final project gives you the opportunity to dive deeply into a parallel systems problem of your choosing for the final month of the course. Perhaps more importantly to some of you, it is your big chance to achieve fame, glory, and prizes at the parallelism competition. What you attempt for your project is completely up to you. There are only two requirements: (1) We want your project to be challenging (you should learn something relevant to the themes of this class) and (2) we want your project to be fun (you should be pumped to work on it)!

Choosing a Project

One common way to choose a project is to design a parallel solution to a problem in an application area that is interesting to you. Projects you have attempted 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. If you can convince the course staff that a parallel programming problem in one of these application domains is sufficiently challenging (that is, the solution to get good speedup is not obvious to you from the start), it's likely it will make a good project.

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 parallel algorithm, and describe which platform is better suited for the job. Alternatively you could choose to evaluate different versions of an algorithm for different architectures. 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.

You may implement your project on any parallel platform. Gates machines, the latedays cluster, GPUs, Blacklight, Amazon EC2, iPhone/iPad/Android SoCs (we have NVIDIA Tegra K1 dev kit if you need one), FPGAs, Raspberry Pi, and architecture simulators are all possible and welcome platforms for projects. We also can give you access to a 32-core (64 hyper-thread) machine in the final week of the project for performance measurement if latedays doesn't meet your needs. Come talk to us if you have exotic computing needs --- sometimes the staff knows about a resource around campus that you might not be aware of.

Below is a random sampling of ideas: (You should also look at the lists of student projects from Spring 2012, Spring 2013, and Spring 2014.

• Projects proposed by Ph.D. students and professors:
  • Parallel rendering of multiple depth images for fast object localization. (more details in the piazza post)
  • Help the UPitt bio lab scale-up molecule visualization. See the project blurb here!
• Application-oriented projects (parallelize an application):
  • Implement a game playing system: Chess, Go, etc.
  • Graphics: extend assignment 2 to achieve high performance under real workloads (render real triangle meshes and more complex shading functions); implement a parallel ray tracer. (those interested in graphics projects should see Kayvon);
  • Physical simulation: implement a high-resolution fluid simulation, rigid body solver, cloth simulation
  • Computer vision: real-time object detection/tracking, image similarity search in a large image database; study algorithms for selective search (see here and here) and implement then very efficiently.
  • Machine Learning on big data. Parallelize a machine learning algorithm (e.g., parallel logistic regression, clustering, large scale classification, deep learning). Also see major software efforts like the Petuum project, MLBase, and the parameter server.
  • Prof. Kayvon be really interested to see if someone could implement parts of the Caffe deep learning framework to Xeon Phi.
  • Prof. Kayvon would also be really interested in someone implementing deep convolutional network evaluation in Halide. Even better would be a drop-in Caffe implementation in Halide. (very challenging, automatic A!)
  • Solvers: Implement a parallel linear solver (using the conjugate-gradient or multi-grid method)
  • Take a look at Guy Blelloch's problem-based parallel algorithm benchmark suite. Any solution that improves on the algorithms in the performance suite would be a great project.
  • Implement an application on an FPGA (see CoRAM)
  • Compare the performance of different parallel algorithms for the same task on different machines (often different algorithms are best for different platforms)
  • Implement a lock-free data structure.
  • Note to students focused on applications for distributed systems. You may want to consider implenting your work in Legion or Spark.
• Languages/runtimes/compilers:
  • Add high-performance parallel bindings to a system like Javascript or Python. e.g., See PyCuda and the great projects in 2014.
  • 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.
  • Add a GPU backend for a subset of GraphLab, or simply improve the existing parallel runtime for clusters or CPUs.
  • Extend your 15-411 compiler to generate parallel code.
  • Add a new feature to the Halide image processing language.
  • Design a mini domain-specific language (or API, or framework) for a problem domain you are interested in. If your interested in building a compiler for a domain specific language definitely take a look at Terra (ask TA Will, he's familiar with it.)
• Systems and analysis projects:
  • Study a workload's amenability to SIMD execution. Simulate behavior given multiple SIMD widths.
  • Modify or analyze a workload using GPGPU Sim, PIN, or talk to Li about other simulation tools.
  • Modify your 15-410 kernel to utilize a parallel machine
  • Investigate parallel implementations of malloc
  • Measure the energy consumption of a parallel computer under various loads: (one example is here)
  • Build a real elastic web server using Amazon's actual services.
  • Measure/analyze the energy consumption on a mobile device while certain applications are running.
  • Implement High performance parallel key-value store.
  • Build a heterogeneous computing system to accelerate the performance of applications using FPGAs.