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- W97318911 abstract "Standards bodies and commercial software vendors have defined parallel constructs to harness the parallelism in computations. Using the task graph model of parallel program execution, we show how common programming constructs that impose series-parallel task dependencies can lead to unbounded slowdown compared to the inherent parallelism in the algorithm. We describe various ways in which this slowdown can be avoided. Inexpensive multicore processors have brought parallelism to the desktop computer [2] and users would like to take advantage of this parallelism for faster program execution. Standards for multiple-processor programming such as OpenCL [7] and commercial numerical software such as Matlab4 and Mathematica5 include language constructs for parallelism. Our position is that these constructs may limit the amount of parallelism, causing slowdown, but we also argue that there are ways to avoid this unnecessary loss in performance. With the projected progression from multicore computing (2-8 cores) to manycore computing (hundreds of cores) [10], we believe that parallel computing systems should avoid slowdown at the point of expressing the intention of the programmer, between the concept and the keyboard. We focus on a specific structure on the dependencies between program tasks which some constructs impose. This structure is called series-parallel and can be most easily expressed as those task graphs generated by the language P ::= seq(P, P ) | par(P, P ) | a where a is a task or activity which represents some amount of program code (possibly as small as a single arithmetic operation) to be executed on one processor. Series-parallel task graphs are not only easy to express, but also have modular structure which can be exploited for efficient scheduling [3,11]. We can represent the tasks in a parallel program, and the dependencies between tasks, in a task graph (also known as an activity network). A task graph is a directed graph where each node is labelled with a distinct activity name, and associated with each activity is a positive real number, its duration, describing how long the activity will take to execute. Arcs in the task graph capture dependencies between tasks (also known as precedence constraints). The left graph in the figure is series-parallel and can be written as seq(par(a, b), par(c, d)). The right graph is not series-parallel a b" @default.
- W97318911 created "2016-06-24" @default.
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- W97318911 date "2010-01-01" @default.
- W97318911 modified "2023-10-07" @default.
- W97318911 title "Performance loss between concept and keyboard" @default.
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