1,246 research outputs found
์ด์ข ๋ฉํฐ ์ฝ์ด ํ๋ก์ธ์์์ SDF/L ๊ทธ๋ํ ์ค์ผ์ค๋ง ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 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.๋ฐ์ดํฐํ๋ก์ฐ ๋ชจ๋ธ์ ์ ํ๋ฆฌ์ผ์ด์
์ ํ์คํฌ๋ฅผ ๋ณ๋ ฌ ์ฒ๋ฆฌํ ๋ ์ข์ ๋ชจ๋ธ๋ก ์๋ ค์ ธ ์์ง๋ง ๋ฐ์ดํฐ๋ฅผ ๋ณ๋ ฌ๋ก ์ฒ๋ฆฌํ๋ ๋ฐ์ ํ์ฉํ๊ธฐ๋ ์ด๋ ต๋ค. ๋ฐ์ดํฐ ์์ค ๋ณ๋ ฌ ์ฒ๋ฆฌ๋ ๋ฃจํ ๊ตฌ์กฐ๋ฅผ ํตํด ํํ๋ ์ ์์ผ๋ ๊ธฐ์กด ๋ฐ์ดํฐํ๋ก์ฐ ๋ชจ๋ธ์์ ๋ช
์์ ์ผ๋ก ๋ฃจํ ๊ตฌ์กฐ๋ ๋ช
์ธํ๋ ๋ฐฉ๋ฒ์ด ์์๋ค. ์ด๋ฌํ ๋จ์ ์ ๊ทน๋ณตํ๊ธฐ ์ํด ๊ณ์ธต์ ๊ตฌ์กฐ๋ฅผ ํ์ฉํ์ฌ ๋ฃจํ ๊ตฌ์กฐ๋ฅผ ๋ช
์์ ์ผ๋ก ๋ช
์ธํ ์ ์๋ SDF/L ๋ชจ๋ธ์ด ์ ์๋์๋ค. ๊ทธ๋ฌ๋ ์ด๊ธฐ์ข
ํ๋ก์ธ์์ ๋ํ SDF/L ๊ทธ๋ํ์ ์ค์ผ์ค๋ง์ ์ด์ ๊น์ง ๊ณ ๋ ค๋์ง ์์ ๊ฒ์ผ๋ก ํ์
๋๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ SDF/L ๋ชจ๋ธ๋ก ํํ๋๋ ์ ํ๋ฆฌ์ผ์ด์
์ ์ด๊ธฐ์ข
ํ๋ก์ธ์์ ๋ํ์ฌ ์ค์ผ์ค๋งํ๋ ๊ธฐ๋ฒ์ ์๊ฐํ๋ค. ์ ์๋ ๋ฐฉ๋ฒ์์๋ ๋จผ์ ์งํ์ ๋ฉํ ํด๋ฆฌ์คํฑ์ ์ฌ์ฉํ์ฌ ํ์คํฌ ๋งคํ์ ํ์ํ๋ค. ์ดํ ํ์ ์์ค์์ ๋ณ๋ ฌ ๋ฃจํ ์ค์ผ์ค์ ๋ง๋ ๋ค์ ์์ ์์ค์์ ์ค์ผ์ค ๊ตฌ์ฑํ ๋ ์ฌ์ฌ์ฉํ๋ ์ํฅ์์ ๊ณ์ธต์ ํ์คํฌ ์ค์ผ์ค๋ง์ ์ํํ๋ค. ์ ์ํ๋ ์ค์ผ์ค๋ง ๊ธฐ๋ฒ์ ํจ์จ์ฑ์ ๊ฒ์ฆํ๊ธฐ ์ํด ๋ฒค์น๋งํฌ ์์ ์ ๋ฌด์์๋ก ์์ฑ๋ SDF/L ๊ทธ๋ํ์ ๊ธฐ๋ฒ์ ์ ์ฉํ์๋ค.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
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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_
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(nยทm), 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
Framework for Simulation of Heterogeneous MpSoC for Design Space Exploration
Due to the ever-growing requirements in high performance data computation, multiprocessor systems have been proposed to solve the bottlenecks in uniprocessor systems. Developing efficient multiprocessor systems requires effective exploration of design choices like application scheduling, mapping, and architecture design. Also, fault tolerance in multiprocessors needs to be addressed. With the advent of nanometer-process technology for chip manufacturing, realization of multiprocessors on SoC (MpSoC) is an active field of research. Developing efficient low power, fault-tolerant task scheduling, and mapping techniques for MpSoCs require optimized algorithms that consider the various scenarios inherent in multiprocessor environments. Therefore there exists a need to develop a simulation framework to explore and evaluate new algorithms on multiprocessor systems. This work proposes a modular framework for the exploration and evaluation of various design algorithms for MpSoC system. This work also proposes new multiprocessor task scheduling and mapping algorithms for MpSoCs. These algorithms are evaluated using the developed simulation framework. The paper also proposes a dynamic fault-tolerant (FT) scheduling and mapping algorithm for robust application processing. The proposed algorithms consider optimizing the power as one of the design constraints. The framework for a heterogeneous multiprocessor simulation was developed using SystemC/C++ language. Various design variations were implemented and evaluated using standard task graphs. Performance evaluation metrics are evaluated and discussed for various design scenarios
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