Design aspects of a distributed MIMD processor

Electrical Engineering, Mathematics and Computer ScieneElectrical Engineering, Mathematics and Computer Scienc

Application-directed voltage scaling

Clock (and voltage) scheduling is an important technique to reduce the energy consumption of processors that support voltage scaling. It is difficult, however, to achieve good results using only statistics from the OS level when applications show bursty (unpredictable) behavior. We take the approach that such applications must be made power-aware and specify their Average Execution Time (AET) and the deadline to the scheduler controlling the clock speed and processor voltage. This paper describes our Energy Priority Scheduling (EPS) algorithm supporting power-aware applications. EPS orders tasks according to how tight their deadlines are and how often tasks overlap. Low-priority tasks are scheduled first, since they can be easily preempted to accommodate for high-priority tasks later. The EPS algorithm does not always yield the optimal schedule, but has a low complexity. We have implemented EPS on a StrongARM-based variable-voltage platform. We conducted experiments with a modified video decoder that estimates the AET of each frame. Measurements show that application-directed voltage scaling reduces processor power consumption with 50% for the bursty video decoder without missing any frame deadlines

Automatic Parallel Program Generation and Optimization from Data Decompositions

this paper a general framework is described for the automatic generation of parallel programs based on a separately specified decomposition of the data. To this purpose, programs and data decompositions are expressed in a calculus, called Vcal. It is shown that by rewriting calculus expressions, Single Program Multiple Data (SPMD) code can be generated for shared-memory as well as distributed-memory parallel processors. Optimizations are derived for certain classes of access functions to data structures, subject to block, scatter, and block/scatter decompositions. The presented calculus and transformations are language independent. 1