Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system

Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed

A communication-ordered task graph allocation algorithm

technical reportThe inherently asynchronous nature of the data flow computation model allows the exploitation of maximum parallelism in program execution?? While this computational model holds great promise several problems must be solved in order to achieve a high degree of program performance?? The allocation and scheduling of programs on MIMD distributed memory parallel hardware is necessary for the implementation of e cient parallel systems?? Finding optimal solutions requires that maxi mum parallelism be achieved consistent with resource limits and minimizing communication costs and has been proven to be in the class of NP complete problems?? This paper addresses the problem of static allocation of tasks to distributed memory MIMD systems where simultaneous computation and communication is a factor?? This paper discusses similarities and di erences between several recent heuristic allocation approaches and identi es common problems inherent in these approaches?? This paper presents a new algorithm scheme and heuristics that resolves the identi ed problems and shows signi cant performance bene ts?

An automatic programming tool for heterogeneous

Recent advances in network technology and the higher levels of circuit integration due to VLSI have led to widespread interest in the use of multiprocessor systems in solving many practical problems. As the hardware continues to diminish in size and cost, new possibilities are being created for systems that are heterogeneous by design. Parallel multiprocessor architectures are now feasible and provide a valid solution to the throughput rates demands of the increasing sophistication of control and/or instrumentation systems. Increasing the number of processors and the complexity of the problems to be solved makes programming multiprocessor systems more difficult and error-prone. This paper describes some parts already implemented (mainly the scheduler) of a software development tool for heterogeneous multiprocessor system that will perform automatically: code generation, execution time estimation, scheduling and handles the communication primitive insertion

Multiprocessor scheduling with practical constraints

The problem of scheduling tasks onto multiprocessor systems has increasing practical importance as more applications are being addressed with multiprocessor systems. Actual applications and multiprocessor systems have many characteristics which become constraints to the general scheduling problem of minimizing the schedule length. These practical constraints include precedence relations and communication delays between tasks, yet few researchers have considered both these constraints when developing schedulers. This work examines a more general multiprocessor scheduling problem, which includes these practical scheduling constraints, and develops a new scheduling heuristic using a list scheduler with dynamically computed priorities. The dynamic priority heuristic is compared against an optimal scheduler and against other researchers’ approaches for thousands of randomly generated scheduling problems. The dynamic priority heuristic produces schedules with lengths which are 10% to 20% over optimal on the average. The dynamic priority heuristic performs better than other researchers’ approaches for scheduling problems with the practical constraints. We conclude that it is important to consider practical constraints in the design of a scheduler and that a simple heuristic can still achieve good performance in this area

A communication-ordered task graph allocation algorithm

technical reportThe inherently asynchronous nature of the data flow computation model allows the exploitation of maximum parallelism in program execution. While this computational model holds great promise, several problems must be solved in order to achieve a high degree of program performance. The allocation and scheduling of programs on MIMD distributed memory parallel hardware, is necessary for the implementation of efficient parallel systems. Finding optimal solutions requires that maximum parallelism be achieved consistent with resource limits and minimizing communication costs, and has been proven to be in the class of NP-complete problems. This paper addresses the problem of static allocation of tasks to distributed memory MIMD systems where simultaneous computation and communication is a factor. This paper discusses similarities and differences between several recent heuristic allocation approaches and identifies common problems inherent in these approaches. This paper presents a new algorithm scheme and heuristics that resolves the identified problems and shows significant performance benefits

Real-Time Scheduling for Software Prototyping

This paper presents several real-time scheduling algorithms developed to support rapid prototyping of embedded systems using the Computer Aided Prototyping System (CAPS). The CAPS tools are based on the Prototyping System Description Language (PSDL), which is a high-level language designed specifically to support the conceptual modeling of real-time embedded systems. This paper describes the scheduling algorithms used in CAPS along with the associated timing constraint and hardware models, which include single and multi-processor configurations