이종 멀티 코어 프로세서에서 SDF/L 그래프 스케줄링 기법

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. Ha Soonhoi.Although dataflow models are known to thrive at exploiting task-level parallelism of an application, it is difficult to exploit the parallelism of data. Data-level parallelism can be represented well with loop structures, but these structures are not explicitly specified in most existing dataflow models. SDF/L model was introduced to overcome this shortcoming by specifying the loop structures explicitly in a hierarchical fashion. To the best of our knowledge however, scheduling of SDF/L graph onto heterogeneous processors has not been considered in any previous work. In this dissertation, we introduce a scheduling technique of an application represented by the SDF/L model onto heterogeneous processors. In the proposed method, we explore the mapping of tasks using an evolutionary meta-heuristic and schedule hierarchically in a bottom-up fashion, creating parallel loop schedules at lower levels first and then re-using them when constructing the schedule at a higher level. To verify the efficiency of the proposed scheduling methodology, we apply it to benchmark examples and randomly generated SDF/L graphs.데이터플로우 모델은 애플리케이션의 태스크를 병렬 처리할 때 좋은 모델로 알려져 있지만 데이터를 병렬로 처리하는 데에 활용하기는 어렵다. 데이터 수준 병렬 처리는 루프 구조를 통해 표현될 수 있으나 기존 데이터플로우 모델에서 명시적으로 루프 구조는 명세하는 방법이 없었다. 이러한 단점을 극복하기 위해 계층적 구조를 활용하여 루프 구조를 명시적으로 명세할 수 있는 SDF/L 모델이 제안되었다. 그러나 이기종 프로세서에 대한 SDF/L 그래프의 스케줄링은 이전까지 고려되지 않은 것으로 파악된다. 본 논문에서는 SDF/L 모델로 표현되는 애플리케이션을 이기종 프로세서에 대하여 스케줄링하는 기법을 소개한다. 제안된 방법에서는 먼저 진화적 메타 휴리스틱을 사용하여 태스크 매핑을 탐색한다. 이후 하위 수준에서 병렬 루프 스케줄을 만든 다음 상위 수준에서 스케줄 구성할 때 재사용하는 상향식의 계층적 태스크 스케줄링을 수행한다. 제안하는 스케줄링 기법의 효율성을 검증하기 위해 벤치마크 예제와 무작위로 생성된 SDF/L 그래프에 기법을 적용하였다.Chapter 1 Introduction 1 Chapter 2 Related Work 6 2.1 SDF Scheduling with Data-level Parallelism 8 2.2 Hierarchical Scheduling 9 Chapter 3 Problem and Challenges 11 3.1 Notations and Problem Description 11 3.2 Challenges 12 Chapter 4 Proposed methodology 15 4.1 Mapping Exploration 15 4.2 Priority Assignment and List Scheduling Heuristic 17 4.3 Hierarchical Scheduling 18 4.4 Complexity 23 Chapter 5 Experiments 24 5.1 Benchmarks 25 5.2 Randomly Generated Graphs 30 Chapter 6 Conclusions 35 Bibliography 37 요 약 41석

Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems

We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a discrete speed set, we propose solving a tractable linear program. Our formulations are based on a fluid model and a global scheduling scheme, i.e. tasks are allowed to migrate between processors. The new methods are compared with three global energy/feasibility optimal workload allocation formulations. Simulation results illustrate that our methods achieve both feasibility and energy optimality and outperform existing methods for constrained deadline tasksets. Specifically, the results provided by our algorithm can achieve up to an 80% saving compared to an algorithm without a frequency scaling scheme and up to 70% saving compared to a constant frequency scaling scheme for some simulated tasksets. Another benefit is that our algorithms can solve the scheduling problem in one step instead of using a recursive scheme. Moreover, our formulations can solve a more general class of scheduling problems, i.e. any periodic real-time taskset with arbitrary deadline. Lastly, our algorithms can be applied to both online and offline scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c); definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations were correct only for special case of p_i=d_

Algorithms for scheduling task-based applications onto heterogeneous many-core architectures

In this paper we present an Integer Linear Programming (ILP) formulation and two non-iterative heuristics for scheduling a task-based application onto a heterogeneous many-core architecture. Our ILP formulation is able to handle different application performance targets, e.g., low execution time, low memory miss rate, and different architectural features, e.g., cache sizes. For large size problem where the ILP convergence time may be too long, we propose a simple mapping algorithm which tries to spread tasks onto as many processing units as possible, and a more elaborate heuristic that shows good mapping performance when compared to the ILP formulation. We use two realistic power electronics applications to evaluate our mapping techniques on full RTL many-core systems consisting of eight different types of processor cores

A Rapid Heuristic for Scheduling Non-Preemptive Dependent Periodic Tasks onto Multiprocessor

International audienceWe address distributed real-time applications represented by systems of non-preemptive dependent periodic tasks. This system is described by an acyclic directed graph. Because the distribution and the scheduling of these tasks onto a multiprocessor is an NP-hard problem we propose a greedy heuristic to solve it. Our heuristic sequences three algorithms: assignment, unrolling, and scheduling. The tasks of the same, or multiple, periods are assigned to the same processor according to a mixed sort. Then, the initial graph of tasks is unrolled, i.e. each task is repeated according to the ratio between its period and the least common multiple of all periods of tasks. Finally, the tasks of the unrolled graph are distributed and scheduled onto the processors where they have been assigned. Then, we give the complexity of this heuristic, and we illustrate it with an example. A performance analysis comparing our heuristic with an optimal Branch and Cut algorithm concludes that our heuristic is effective in terms of scheduling success ratio and speed

Assigning real-time tasks on heterogeneous multiprocessors with two types of processors

Consider the problem of scheduling a set of implicitdeadline sporadic tasks on a heterogeneous multiprocessor so as to meet all deadlines. Tasks cannot migrate and the platform is restricted in that each processor is either of type-1 or type-2 (with each task characterized by a different speed of execution upon each type of processor). We present an algorithm for this problem with a timecomplexity of O(n·m), where n is the number of tasks and m is the number of processors. It offers the guarantee that if a task set can be scheduled by any non-migrative algorithm to meet deadlines then our algorithm meets deadlines as well if given processors twice as fast. Although this result is proven for only a restricted heterogeneous multiprocessor, we consider it significant for being the first realtime scheduling algorithm to use a low-complexity binpacking approach to schedule tasks on a heterogeneous multiprocessor with provably good performance