Scheduling DAG's for asynchronous multiprocessor execution

A new approach is given for scheduling a sequential instruction stream for execution “in parallel” on asynchronous multiprocessors. The key idea in our approach is to exploit the fine grained parallelism present in the instruction stream. In this context, schedules are constructed by a careful balancing of execution and communication costs at the level of individual instructions, and their data dependencies. Three methods are used to evaluate our approach. First, several existing methods are extended to the fine grained situation. Our approach is then compared to these methods using both static schedule length analyses, and simulated executions of the scheduled code. In each instance, our method is found to provide significantly shorter schedules. Second, by varying parameters such as the speed of the instruction set, and the speed/parallelism in the interconnection structure, simulation techniques are used to examine the effects of various architectural considerations on the executions of the schedules. These results show that our approach provides significant speedups in a wide-range of situations. Third, schedules produced by our approach are executed on a two-processor Data General shared memory multiprocessor system. These experiments show that there is a strong correlation between our simulation results, and these actual executions, and thereby serve to validate the simulation studies. Together, our results establish that fine grained parallelism can be exploited in a substantial manner when scheduling a sequential instruction stream for execution “in parallel” on asynchronous multiprocessors     

NETWORKING WITH A HUMAN FACE Mentoring as a Career Strategy

Mentoring has been around for ages, often masquerading as the management club or the fast track. Among successful business unit managers, mentoring is a well-understood practice, and it's a career development strategy today's IS professional would be wise to consider.     

Algebra of algorithms and Kaluzhnin's graphschemas

This research was supported under project No. 6.02.02/128-93 as part of the state scientific-technical program on future information technologies and systems by the Ukrainian State Committee, of Science and Technology.     

Review of the Robotica software package for robotic manipulators

Robotica is a computer-aided design package for robotic nmanipulators developed at the Coordinated Science Laboratory at the University of Illinois at Urbana- Champaign. The package is a collection of function definitions for the Mathematica symbolic mathematics program. Robotica can be used either with an X- Windows graphical user interface (GUI) on a Sun Workstation or as an included function definition file within Mathematica. The primary feature of Robotica is the ability to compute, symbolically or numerically, the kinematic and dynamic equations of arbitrary robot systems utilizing the standard Denevit-Hartenburg (DH) kinematic convention. Robotica also provides the ability to visualize these arbitrary manipulators using the X- Windows graphical interface to the Mathematica graphics routines. The paper looks at the usage of Robotica at the Air Force Institute of Technology, comments on the features of Robotica, and needs for improvement and suggestions for future development