Architecture of Computing Systems (Lecture Notes in Computer ...

Recommendations

Info

56 J. Zeppenfeld and A. Herkersdorf 3 Simulation Results Given the network processor MP-SoC presented above, this section presents simulation results that compare a classical system implementation with one enhanced by an autonomic layer. In order to show the adaptive nature of the autonomic system, various types of packet bursts, each consisting of 1000 packets, are sent in alternating fashion to the input MAC for processing. The packet inter-arrival rate is fixed at 4 μs, resulting in a burst duration of 4 ms independent of the packet processing duration. For each of the incoming burst scenarios, the system must either adapt to meet the processing requirements, or maintain a sufficient reserve of processing power to be able to cope with even the largest and most processing intensive packets. All systems used below are based on a common base system configuration. The hardware components and their functionality, as well as the software tasks used for packet processing were described in sections 2.1 and 2.2, respectively. Unless stated otherwise, the initial task distribution is such that one CPU is responsible for ingress path processing (Tasks 1 and 2), one CPU is responsible for payload processing (Task 3), and the third CPU is responsible for egress path processing (Tasks 4 and 5). The initial CPU frequencies are chosen such that the system would be able to handle all burst scenarios without requiring parameter adaptation. Those systems containing autonomic enhancements use the monitors, actuators and LCT evaluator presented in section 2.3. 3.1 Comparison of Autonomic and Static Systems The simulation results shown in Figure 5 compare the objective value and frequency adjustment of four different system configurations. The first two systems are static without any autonomic enhancements, where the first corresponds to the base system configuration described above. In the second static system, both the task distribution and CPU frequency were hand optimized with prior knowledge of the incoming packet traffic. The results of this optimized system demonstrate how an ideally parameterized static system compares to a system with autonomic enhancements. The two autonomic systems are both based on the common system configuration, but are differentiated by the monitor signals available to them. While the second system uses the AE interconnect to share workload information globally among the CPU AEs, the first autonomic system must base its optimization decisions solely on local monitor information. Both systems remain capable of migrating tasks, however. In comparison to a static system, the trend of the objective value clearly shows the benefits of an autonomic system regarding fulfillment of the objective function chosen in section 2.3.3. The autonomic system with global information is able to maintain system operation within approximately 10% of the optimum for all incoming traffic scenarios. Although the hand optimized static system is able to top this for the fifth and most processing intensive traffic scenario, it is not able to maintain such a high level of optimization across all bursts. This is a direct consequence of the fact that a system optimized for one scenario is not necessarily optimized for other scenarios. Whereas the designer must choose a certain scenario for which the static system
Objective Value Avg. Frequency (MHz) 35% 30% 25% 20% 15% 10% 5% 0% 160 140 120 100 80 60 40 20 0 0 s 0 s Autonomic Workload Management for Multi-core Processor Systems 57 4 ms 4 ms 8 ms 8 ms 12 ms 12 ms 16 ms 16 ms 20 ms 20 ms 24 ms 24 ms 28 ms 28 ms Static Static Opt. Auto Local Auto Global Lower is better Static Static Opt. Auto Local Auto Global Fig. 5. Objective value and average CPU frequency of static and autonomic systems across various traffic scenarios. Lower values are better. is optimized during design time, the autonomic system can optimize itself during run time to whatever scenario it is confronted with. Not only does this provide decent optimization for a much larger set of foreseeable and unforeseeable scenarios, it also relieves the designer of having to optimize all aspects of the system at design time. Examining the trend of the average CPU frequency confirms the problem that a static system must meet the worst case processing requirements in order to remain functional over all workload scenarios, thereby wasting processing power during periods of lower activity. Except for processing of the most workload intensive fifth burst, the autonomic system is able to achieve an equal or lower average CPU frequency than the hand optimized system. The benefits of global information also become visible here, as the locally optimized system is not able to share the workload across the CPUs as effectively, requiring an increased average CPU frequency to meet the processing requirements.
Page 2 and 3:
Lecture Notes in Computer Science 5
Page 4 and 5:
Volume Editors Christian Müller-Sc
Page 6 and 7:
General Chair Organization Christia
Page 8 and 9:
Organization IX Hartmut Schmeck Kar
Page 10 and 11:
Keynote Table of Contents HyVM - Hy
Page 12 and 13:
Table of Contents XIII JetBench:AnO
Page 14 and 15:
How to Enhance a Superscalar Proces
Page 16 and 17:
4 J. Mische et al. The Real-time Vi
Page 18 and 19: 6 J. Mische et al. 4.1 Instruction
Page 20 and 21: 8 J. Mische et al. Additionally the
Page 22 and 23: 10 J. Mische et al. % of unused pip
Page 24 and 25: 12 J. Mische et al. % of cycles spe
Page 26 and 27: 14 J. Mische et al. 16. Lickly, B.,
Page 28 and 29: 16 G. Aşılıoğlu, E.M. Kaya, and
Page 38 and 39: 26 T.B. Preußer, P. Reichel, and R
Page 50 and 51: 38 P. Bellasi, W. Fornaciari, and D
Page 62 and 63: 50 J. Zeppenfeld and A. Herkersdorf
Page 74 and 75: 62 B. Jakimovski, B. Meyer, and E.
Page 86 and 87: 74 M. Bonn and H. Schmeck Fig. 1. J
Page 88 and 89: 76 M. Bonn and H. Schmeck 2.2 Node
Page 90 and 91: 78 M. Bonn and H. Schmeck Uptime-ba
Page 92 and 93: 80 M. Bonn and H. Schmeck 2.4 Simul
Page 94 and 95: 82 M. Bonn and H. Schmeck done rate
Page 96 and 97: 84 M. Bonn and H. Schmeck Fig. 8. J
Page 98 and 99: 86 M. Bonn and H. Schmeck tells the
Page 100 and 101: 88 J.-P. Steghöfer et al. � �
Page 102 and 103: 90 J.-P. Steghöfer et al. mechanis
Page 104 and 105: 92 J.-P. Steghöfer et al. resource
Page 106 and 107: 94 J.-P. Steghöfer et al. Choose a
Page 108 and 109: 96 J.-P. Steghöfer et al. 1. Defin
Page 110 and 111: 98 J.-P. Steghöfer et al. and all
Page 112 and 113: 100 J.-P. Steghöfer et al. 19. Kim
Page 114 and 115: 102 K. Kloch et al. large-scale sys
Page 116 and 117: 104 K. Kloch et al. a�t� 1.0 0.
Page 118 and 119:
106 K. Kloch et al. constant. This
Page 120 and 121:
108 K. Kloch et al. Relative number
Page 122 and 123:
110 K. Kloch et al. (a) infection r
Page 124 and 125:
112 K. Kloch et al. (ii) Phase of a
Page 126 and 127:
114 P. Petoumenos et al. Studying t
Page 128 and 129:
116 P. Petoumenos et al. % of Misse
Page 130 and 131:
118 P. Petoumenos et al. IQ: n 4-in
Page 132 and 133:
120 P. Petoumenos et al. downsizing
Page 134 and 135:
122 P. Petoumenos et al. As long as
Page 136 and 137:
124 P. Petoumenos et al. comparable
Page 138 and 139:
Exploiting Inactive Rename Slots fo
Page 140 and 141:
128 M. Kayaalp et al. In a supersca
Page 142 and 143:
130 M. Kayaalp et al. INSTRUCTION 1
Page 144 and 145:
132 M. Kayaalp et al. time. Alterna
Page 146 and 147:
134 M. Kayaalp et al. Fig. 3. Numbe
Page 148 and 149:
136 M. Kayaalp et al. The results o
Page 150 and 151:
Efficient Transaction Nesting in Ha
Page 152 and 153:
140 Y. Liu et al. HTMs include TCC
Page 154 and 155:
142 Y. Liu et al. rollback T0 begin
Page 156 and 157:
144 Y. Liu et al. Processor core Pr
Page 158 and 159:
146 Y. Liu et al. 5.2 Results and A
Page 160 and 161:
148 Y. Liu et al. decreases, partia
Page 162 and 163:
Decentralized Energy-Management to
Page 164 and 165:
152 B. Becker et al. Furthermore, t
Page 166 and 167:
154 B. Becker et al. An optimizing
Page 168 and 169:
156 B. Becker et al. smart-home man
Page 170 and 171:
158 B. Becker et al. freedom like w
Page 172 and 173:
160 B. Becker et al. Power [W] 4500
Page 174 and 175:
EnergySaving Cluster Roll: Power Sa
Page 176 and 177:
164 M.F. Dolz et al. Themodulequeri
Page 178 and 179:
166 M.F. Dolz et al. This daemon al
Page 180 and 181:
168 M.F. Dolz et al. been submitted
Page 182 and 183:
170 M.F. Dolz et al. On the other h
Page 184 and 185:
172 M.F. Dolz et al. at the inactiv
Page 186 and 187:
Effect of the Degree of Neighborhoo
Page 188 and 189:
176 T. Abdullah et al. A zone based
Page 190 and 191:
178 T. Abdullah et al. A consumer/p
Page 192 and 193:
180 T. Abdullah et al. Messages 140
Page 194 and 195:
182 T. Abdullah et al. (except when
Page 196 and 197:
184 T. Abdullah et al. % Matchmakin
Page 198 and 199:
186 T. Abdullah et al. show that th
Page 200 and 201:
188 M. Schindewolf, D. Kramer, and
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
200 R. Plyaskin and A. Herkersdorf
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
212 M.Y. Qadri, D. Matichard, and K
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
A Tightly Coupled Accelerator Infra
Page 236 and 237:
224 F. Nowak and R. Buchty where A
Page 238 and 239:
226 F. Nowak and R. Buchty Fig. 3.
Page 240 and 241:
228 F. Nowak and R. Buchty Table 2.
Page 242 and 243:
230 F. Nowak and R. Buchty Table 4.
Page 244 and 245:
232 F. Nowak and R. Buchty 5.3 Comp
Page 246 and 247:
Optimizing Stencil Application on M
Page 248 and 249:
236 F. Xudong et al. compared to th
Page 250 and 251:
238 F. Xudong et al. stencil comput
Page 252 and 253:
240 F. Xudong et al. threads in the
Page 254 and 255:
242 F. Xudong et al. Speedup 14 12
Page 256 and 257:
244 F. Xudong et al. 5 Related Work
Page 258:
Abdullah, Tariq 174 Alima, Luc Onan
show all

Architecture of Computing Systems (Lecture Notes in Computer ...

Create successful ePaper yourself

Delete template?

Save as template?