SIMD-MIMD Real-Time Comparisons (Chapter 7)

1. SIMD-MIMD Real-Time Comparisons(Chapter 7) • References: • Stankovic, Spuri, Ramamritham, Buttazzo, “Deadline Scheduling for Real-Time Systems”, Kluwer, 1998, ISBN 0-7923-8269-2. • Stankovic, Spuri, Natale, Buttazzo, “Implications of Classical Scheduling Results for Real-Time Systems”, IEEE Computer, Vol. 28, No 6, pp. 16-25, June 1995. • Meilander, Jin, Baker, “Tractable Real-Time Air Traffic Control Automation”, Fourteenth IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS’02), pp. 483-488, November 2002. • Importance of SIMD Computation Reconsidered, Meilander, Baker, Jin, International Parallel and Distributed Processing Symposium (IPDPS),Workshop on Massively Parallel Processing. • Initially we will go through most of the slides used for the presentation of the paper, “Tractable Real-Time Air Traffic Control Automation”. • Presentation was given by Will Meilander at PDCS Conference in November 2002. • The PDCS slides will follow this set of slides. • This slides describe a polynomial time solution for the ATC problem. • A polynomial time multiprocessor (MP) solution to the ATC is not believed possible. In particular, • MP solutions to virtually all real-time problems today include an online solution to one or more dynamic scheduling problems. Chapter 2

2. Most dynamic scheduling problems are NP-hard. • The MP cannot use static scheduling to solve most real-time problems (to avoid using dynamic scheduling). • Observation: A widely accepted principle in parallel computing is that MIMDs (or MPs) are more powerful than SIMDs. From a modeling point of view, this would mean that a MIMD could simulate a SIMD (of the same relative “size”) in constant time. • Since SIMD processors are small ALU units, a fair interpretation of “same size” is needed. • In general, only sketchy reasons are given for this claim such as • A MIMD is a SIMD with fewer restrictions (i.e. no synchronization requirements) so anything a SIMD can do, a MIMD can do in no more time. • Many MIMDs have extra hardware to provide fast synchronization to allow efficient simulation of SIMDs. (See [25, Kumar et. al.] SIMD topics) • Each MIMD processor can execute the SIMD program and synchronize at points where this is required. Chapter 2

3. A discussion along this line occurs in [31, Smith, The Design and Analysis of Parallel Algorithms, pgs 62-65. • Due to mass production, general purpose computers can be used as MIMD processors, but SIMD computers (due to low usage) have to be specially designed. Chapter 2

4. If an AP (an enhanced SIMD) can solve the ATC problem in polynomial time, but no such solution is expected for a MIMD computer, then this casts doubt on a MIMD being able to efficiently simulate an AP. • Since a SIMD can simulate an AP relatively efficiently, the preceding bullet seems to raise doubts as to whether the simulation of a SIMD by a MIMD can be very efficient. • This work raises other open questions as well, both practical and theoretical. • Can a MASC model be built that can efficiently execute current data parallel solutions for MPs but which avoid some additional work that MPs do (in addition to executing the solution steps). • Dynamic scheduling of tasks • Load balancing • Synchronization • Cache/memory coherency problems involving keeping multiple copies of data. • In particular, how can the multiple instruction streams for MASC interact so as to avoid this MP difficulty? Chapter 2