چكيده به لاتين
Abstract:
Reconfigurable computing units consist of a software and hardware resources and are able to do multiple complex tasks simultaneously. This feature made them increasingly popular in recent years, because on one hand, it has accelerate the speed of executing complex tasks using hardware resources and On the other hand, due to the possibility of changing the configuration of the chip during the operation, it has very high flexibility to run complex applications.
In this thesis a new method for scheduling and placement of tasks on the reconfigurable computing unit is offered. In the proposed method the size of the tasks is changed and for each task we consider several different versions. This changing size is a trade-off between execution time and amount of hardware resources needed for a task so that ultimately the multiplication of these two parameters remain the same in different versions of a task. This feature increases flexibility and performance of the algorithm.
For placement and scheduling of tasks with resizing features we offer two methods of history start and history finish, either of these two methods were simulated in both continuous and discrete modes.
According to the results of the simulations, history start method has better performance than the history finish method in high workload. While by increasing the value of U and reducing the workload, the performance of history finish method is better than other proposed method, but both methods show better improvement than the multishape algorithm in low workload.
In low workload(u=2.5), the history start finish has improved the rejection rate for 51.56% and make span for 36.92% and utility for 2.73%, at the same workload the history finish method has improved the rejection rate for 57% and make span for 50.17% and utility for 6.14%. in high workload(u=1.5), ), the history start finish has improved the rejection rate for 27.29% and make span for 20% and utility for 1.2%, at the same workload the history finish method has improved the rejection rate for 10.14% and make span for 7.5% and utility for5.06%. Of course, improvement of suggested methods also depends on other parameters such as slack and workload and the RPU width.
Keywords: reconfigurable computing unit, placement algorithm, scheduling algorithm, multiple variant tasks
