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CS612 Term Projects. http://www.cs.cornell.edu/courses/ cs612/2002sp/projects/. Expectations. picking a partner(s) that you can work with, meeting with your project supervisor at least once a week. doing background reading on the topic,
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CS612 Term Projects http://www.cs.cornell.edu/courses/ cs612/2002sp/projects/
Expectations • picking a partner(s) that you can work with, • meeting with your project supervisor at least once a week. • doing background reading on the topic, • designed novel algorithms or systems to solve the problem, • building an prototype implementation of the system, • presenting your work and results to the class, • writing and submit a report about your activities, and • [optionally] developing a web page that describes your work.
Project Deadlines • Friday, March 26th -- Signup for a project • There will be two presentations on each of these dates. • 4/25 -???, then ??? • 4/30 -???, then ??? • 5/1 - ???, then ??? • Friday, May 3rd -- Turn in your final reports and implementations.
The Projects • Tile Size Selection • Empirical Optimization of Sparse Kernels • Value Prediction • Language Extensions for the Mobile Object Layer • Computationally Intensive Mobile Computing • Computing Reuse Distance Exactly • Design your own project
Tile Size Selection State of the art in tiling imperfectly nested codes • Product space approach can be used for converting imperfect to perfect nesting • Framework can be used for tiling imperfectly nested loops • Heuristics exist for selecting tile sizes for perfectly nested loops. It remains for someone to put it all together.
Tile Size Selection The goals of the project are the following :- • Develop a tile-size determination algorithm for imperfectly-nested loops. • Compare the performance of the code obtained by tiling with the tile-size predicted by your algorithm to the performance with the "optimal" tile-size.
Empirical Optimization of Sparse Kernels • MVM has very little reuse. • Toledo: restructuring Sparse MVM can double its performance on RS6000. • Sparse MVM is often MMM, which can be blocked. But, block size is determined by sparsity and blocks are very small.
Empirical Optimization of Sparse Kernels Project goals: • Evaluate the effective of Toledo’s optimizations on a non-RS6000 architecture. • Use an empirical optimization framework (e.g., ATLAS) to generate highly optimized kernels for performing small MMM’s. • Evaluate the effectively of combined optimizations on a number of different sparse matrices.
Value Prediction • 80% of load values in some codes can be predicted using relatively simple schemes. • E.g., the address of the load instruction to determine the predicted value; for example, the processor can guess that the value will be the same as the value that was last returned by that load instruction • However, we are not aware of any careful studies that explain why value prediction works so well on such codes
Value Prediction Novice project goals: • Understand the state of the art in value prediction • Design and evaluate a value prediction scheme Advanced project goals: • Understand how compiler technology can be used to improve the performance of value prediction. • Implement a prototype compiler system to implement your strategies.
Language Extensions for the Mobile Object Layer The Mobile Object Layer (MOL), • Part of the Portable Runtime Environment for Mobile Applications (PREMA) • Supports a global distributed object space • Provides correct and efficient protocols for message forwarding and communication between migrating objects. • Designed to work with dynamic load balancing libraries. • Lean, language-independent. Easy to port and maintain.
Current interface uses C Ineffective for C++ Class declarations Object creation Language Extensions for the Mobile Object Layer // Params are passed to main()mol_init(argc, argv); mol_msg_handler_t handlers[] = {myHandler};mol_register_msg_handlers(handlers, 1);Foo* pFoo = new Foo();mol_mobile_ptr_t mp = mol_create_mobile_ptr(pFoo); struct Foo { ... void method(void* pParam); ... }; // This handler must be defined// externally to the struct, or else // be static. void myHandler(int nSrc, mol_mobile_ptr_t mp, void* pObject, void* pData, int nSize, void* pArg) { Foo* pFoo = (Foo*)pObject; pFoo->method(pData); } • Method invocation void* pData = <something>;mol_message(mp, myHandler, pData, <data size>, MOL_DELAYED_HANDLER, NULL);
Language Extensions for the Mobile Object Layer • What would be nice: • Declaration/creation MobileObject<Foo>* pObject = new MobileObject<Foo>; • Method invocation pObject->method(pData);
Language Extensions for the Mobile Object Layer The goals of the project are the following: • Design and implement a C++-friendly interface for the MOL. • Either template based, or • Using syntactic extensions. • Compare C++ programs written using the C-style interface with your C++ interface
Computationally Intensive Mobile Computing Remote Local Should we execute locally or remotely?
Computationally Intensive Mobile Computing The goals of the project are the following :- • Develop a simple model for predicting the performance of local and remote execution. • Build a restructuring compiler to generate programs for local and remote execution.
Computing Reuse Distance Exactly • Can we compute reuse distance exactly at compile time?
Computing Reuse Distance Exactly The goals of the project is to develop a method for using Presberger formulae for exactly computing the reuse distance between references in a simple loop nest.
Design your own project Several critera, • It must be developed with oversight from Paul. • It must be substantial enough to keep you busy until the end of the semester, but not so ambitious that you cannot possibly complete it on time. • It must have something to do with compilers, scientific computing, or high-performance computing.
Threaded-C Project idea:Restructuring compilerto parallelize loops to Threaded-C