Atjaunināt sīkdatņu piekrišanu

E-grāmata: Systematic Methodology for Real-Time Cost-Effective Mapping of Dynamic Concurrent Task-Based Systems on Heterogenous Platforms

  • Formāts: PDF+DRM
  • Izdošanas datums: 26-Aug-2007
  • Izdevniecība: Springer-Verlag New York Inc.
  • Valoda: eng
  • ISBN-13: 9781402063442
  • Formāts - PDF+DRM
  • Cena: 106,47 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: PDF+DRM
  • Izdošanas datums: 26-Aug-2007
  • Izdevniecība: Springer-Verlag New York Inc.
  • Valoda: eng
  • ISBN-13: 9781402063442

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

A genuinely useful text that gives an overview of the state-of-the-art in system-level design trade-off explorations for concurrent tasks running on embedded heterogeneous multiple processors. The targeted application domain covers complex embedded real-time multi-media and communication applications. This material is mainly based on research at IMEC and its international university network partners in this area over the last decade. In all, the material those in the digital signal processing industry will find here is bang up-to-date.

Systematic Methodology for Real-Time Cost-Effective Mapping of Dynamic Concurrent Task-Based Systems on Heterogeneous Platforms gives an overview of the state-of-the-art in system-level design trade-off explorations for concurrent tasks running on embedded heterogeneous multiple processors. The targeted application domain covers complex embedded real-time multi-media and communication applications.Many of these applications are concurrent in the sense that multiple subsystems can be running simultaneously. Also, these applications are so dynamic at run-time that the designs based on the worst case execution times are inefficient in terms of resource allocation (e.g., energy budgets). A novel systematical approach is clearly necessary in the area of system-level design for the embedded systems where those concurrent and dynamic applications are mapped. This material is mainly based on research at IMEC and its international university network partners in this area in the period 1997-2006. In order to deal with the concurrent and dynamic behaviors in an energy-performance optimal way, we have adopted a hierarchical system model (i.e., the gray-box model) that can both exhibit the sufficient detail of the applications for design-time analysis and hide unnecessary detail for a low-overhead run-time management. We have also developed a well-balanced design-time/run-time combined task scheduling methodology to explore the trade-off space at design-time and efficiently handle the system adaptations at run-time. Moreover, we have identified the connection between task-level memory/communication management and task scheduling and illustrated how to perform the task-level memory/communication management in order to obtain the design constraints that enable the this connection. A fast approach is also shown to estimate at the system-level, the energy and performance characterization of applications executing on the target platform processors.
1 Introduction
1
1.1 The System-on-Chip Era
2
1.2 Characteristics of Embedded Software
5
1.3 Context and Motivation
9
1.4 TCM Framework
12
1.5 Overview of
Chapters
13
2 Related Work
15
2.1 Real-time Scheduling
15
2.1.1 Static Scheduling
16
2.1.2 Fixed or Dynamic Priority Scheduling
17
2.1.3 Dynamic Scheduling
19
2.1.4 Real-Time Operating Systems
21
2.1.5 Comparison with TCM Scheduling
22
2.2 Low-power Considerations
22
2.2.1 Dynamic Power Management
22
2.2.2 Dynamic Voltage Scaling
23
2.2.3 Battery Life Related
28
2.2.4 Physical Low-power Implementation
28
2.2.5 Other Approaches
28
2.2.6 Comparison with TCM Low-power Scheduling
29
2.3 Platform Issues and Codesign Framework
29
2.3.1 Processor Architecture
29
2.3.2 Data Memory Organization
30
2.3.3 Data Communication
30
2.3.4 Energy/Performance Estimation
30
2.3.5 Timing Analysis
31
2.3.6 Simulators
31
2.3.7 Co-Synthesis Workflows
32
3 System Model and Work Flow
35
3.1 Overview of TCM Work flow
35
3.2 Gray-Box Model
37
3.2.1 Thread Frame Identification
38
3.2.2 Thread Node Identification
40
3.3 System Scenario Selection
43
3.3.1 Handling of Intra-TF Scenarios
43
3.3.2 Handling of Inter-TF Scenarios
45
3.4 Two-Phase Scheduling
45
3.5 Summary
48
4 Basic Design-Time Scheduling
51
4.1 Problem Formulation
51
4.2 Exact Scheduling Algorithms
53
4.3 Forward Search Algorithm
55
4.3.1 The Kernel Heuristic
55
4.3.2 Tuning the Load Calculation
59
4.3.3 Tuning the Processor Selection Priority
60
4.3.4 Tuning both Load Calculation and Processor Priority
62
4.3.5 Improving Node Selection Policy
64
4.3.6 Comparing ASAP—ACAP and ASAP
67
4.3.7 Pruning Techniques for Tie-Breaking
69
4.3.8 Further Pruning Technique for Tie-Breaking
73
4.3.9 Extension with Exhaustive Search
75
4.4 Backward Search Algorithm
89
4.4.1 Experiment with Fig. 4.11
94
4.4.2 Experiment with Fig. 4.35
96
4.4.3 Summary
98
4.5 Subplatform Scheduling
98
4.5.1 Experiment with Fig. 4.11
99
4.5.2 Experiment with Fig. 4.35
103
4.5.3 Summary
103
4.6 Handling Timing-Constraints
104
4.7 Summary
107
5 Scalable Design-Time Scheduling
109
5.1 Introduction
109
5.2 Motivational Example
110
5.3 Thread Frame Decomposition
114
5.3.1 Problem Formulation
115
5.3.2 Decomposition Guidelines
116
5.3.3 Valid Decomposition
120
5.3.4 Thread Frame Decomposition Algorithm
122
5.4 Thread Partition Clustering
123
5.4.1 Identifying Thread Partition Clusters
123
5.4.2 Generating New Pareto Curves
123
5.5 Thread Partition Interleaving
124
5.5.1 Motivation
124
5.5.2 Preliminaries
125
5.5.3 Interleaving Technique
125
5.6 Experimental Results and Discussions
128
5.6.1 Experimental Setup
128
5.6.2 Experiments with Random Thread Frames
128
5.7 Comparison with State of the Art
130
5.8 Summary
133
6 Fast and Scalable Run-time Scheduling
135
6.1 Two-Phase Task Scheduling: Why and How
135
6.2 Run-Time Scheduling Algorithm
141
6.2.1 Application Model
141
6.2.2 Problem Formulation
142
6.2.3 Greedy Heuristic
143
6.3 Experimental Results
146
6.3.1 Randomly Generated Test Cases
146
6.3.2 Real-Life Applications
148
6.4 Summary
150
7 Handling of Multidimensional Pareto Curves
151
7.1 Overview of the Customized Run-Time Management
152
7.2 Problem Formulation of Run-Time Operating Point Selector
155
7.3 Related Work
156
7.4 MP-SoC Heuristic Description
157
7.4.1 Pareto Filtering Preprocessing
158
7.4.2 Multidimension Resource Reduction
158
7.4.3 Pareto Point Sorting
158
7.4.4 Greedy Algorithm to Solve MMKP
159
7.5 Experimental Results
160
7.6 Summary
162
8 Run-Time Software Multithreading
163
8.1 Motivation of Run-Time Rescheduling
164
8.2 Run-Time Interleaving
166
8.2.1 Preliminaries
167
8.2.2 Interleaving Technique
168
8.3 Experimental Results and Discussion
173
8.4 Comparison with State of the Art
174
8.4.1 Pure Dynamic Scheduling
174
8.4.2 Hybrid Dynamic Scheduling
175
8.5 Summary
176
9 Fast Source-level Performance Estimation
177
9.1 Introduction
177
9.2 Motivational Example
179
9.3 Comparison with State of the Art
182
9.4 Fundamentals of the Estimation Technique
184
9.4.1 Determining Single-Execution Costs
185
9.4.2 Determining Execution Counts
189
9.5 Experimental Results
191
9.6 Summary
193
10 Handling of Task-Level Data Communication and Storage 195
10.1 Memory Architecture
196
10.1.1 Multilayer Memory Architecture in a Multiprocessor Environment
197
10.1.2 Modeling Energy Consumption
198
10.2 Exploring Thread Node Level Data Reuse
200
10.3 Data Assignment on L1 Memory Layer
201
10.3.1 Access Conflicts Degrade Energy and Performance
202
10.3.2 Removing access conflicts with data assignment
204
10.3.3 Energy Aware Data Assignment
207
10.3.4 Evaluating SRAM Energy/Performance Trade-offs
209
10.4 Bandwidth Aware Scheduling
212
10.4.1 Bandwidth Aware Thread Node Scheduling
213
10.4.2 Experimental Results
215
10.5 Handling inter-TN and inter-TF Data Transfers
219
10.5.1 Handling inter-TN Accesses Within a Single TF
219
10.5.2 Inter-Thread Frame Transfers
221
10.6 Summary
223
11 Demonstration on Heterogeneous Multiprocessor SoCs 225
11.1 Motivation for Heterogeneous Multiprocessor Platforms
225
11.2 Mapping Visual Texture Coding Decoder
226
11.2.1 Overview of VTC Decoder
226
11.2.2 Target Platform for VTC Decoder
228
11.2.3 TCM Methodology Illustrated on VTC Decoder
229
11.3 Summary
236
12 Conclusions and future research work 239
A Input and output data of scheduling examples in Section 4.3.1 243
References 249


Francky Catthoor is a leading researcher at IMEC and is very well established within the EDA community. He is IEEE Fellow and has edited and authored 6 books for Springer/Kluwer.