Master Thesis - ICS

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Computer Science Department Antonis Misargopoulos I-SOFT Applications which couple supercomputers remote instruments, immersive environments, data systems Developed by users, static capacity information used for scheduling some applications Centralized scheduler maintained user queues and static capacities, application served as “first come, first served” IOS Real-time, iterative, automatic target recognition applications Applications represented as a dependency graph of subtasks, each of which can be assigned one of possible algorithms Off-line genetic algorithm indexed by dynamic parameters used to determine mapping for current iteration SPP(X) Base serial language X and structured coordination language Compositional performance model based on skeletons associated with program structure Determination of performance model for candidate schedules with minimal execution time Dome C++ extended programs Program rebalanced based on past performance, after some numbers of Dome operations Globally-controlled or locally-controlled load balancing Figure 3.1: Some efforts on developing high-performance Grid scheduler Figure 3.1 summarizes major current efforts in developing high-performance grid schedulers. The way in which the scheduling model is developed for each of these projects, illustrates the different decisions that can be made, and experience with application will give some measure of the effectiveness of these decisions. In the next sections, we discuss these efforts from the perspective of the constituent components of their scheduling models. Program Model Program models for high-performance schedulers generally represent a program by a (possibly weighted) dataflow-style program graph, or by a set of program characteristics that may (or may not) include a structural task dependency graph. Dataflow-style program graphs are a common representation for grid systems. Dome[85] and SPP(X) [83] provide a language abstraction for the program which is compiled into a low-level dependency graph representation. MARS [86] assumes programs to be phased (represented by a sequence of program stages or phases), and 30
Computer Science Department Antonis Misargopoulos builds a program dependency graph as part of the scheduling process. SEA [87] and VDCE [88] represent the program as dependency graphs of coarse-grained tasks. In the case of VDCE, each of the tasks are from a mathematical task library. In other efforts, the program is represented by its characteristics. AppLeS [89] and I-SOFT [90] take this approach, representing programs in term of their resource requirements. AppLeS and I-SOFT focus on coarse-grained grid applications. IOS [91] on the other hand, represents real-time fine-grained iterative automatic target recognition applications. Each task in IOS is associated with a set of possible image-processing algorithms. This approach combines both the program graph and resource requirements approaches to program modelling. Performance Model The performance model provides an abstraction for the behavior of the application on the underlying system. The values of the performance model are predictions of application performance within a given timeframe. Performance models of current efforts in highperformance schedulers represent a wide spectrum of approaches, although parameterization of these models by both static and dynamic information is common. Approaches differ in terms of who supplies the performance model (the system, the programmer, or some combination), its form, and its parameterization. On one end of the spectrum are scheduler-derived performance models. SPP(X), MARS, Dome, IOS, and VDCE require little intervention from the user in order to designate the performance model to be followed. On the other hand, there are the userderived performance models. For instance AppLeS assumes that the performance models will be provided by the user, whereas for the I-SOFT scheduler both the performance model and the resulting schedule is considered to be determined by the user. Nevertheless, some approaches combine both programmer-provided and scheduler-provided performance components. Prophet [92] provides a more generic performance model (ExecutionTime = Computation + Communication) parameterized by benchmark, static, and dynamic capacity information. SEA [87] uses its dataflow-style program graph as input to an expert system which evaluates which task is currently “ready”. These approaches require both programmer and scheduler information. 31
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