Hierarchical Scheduling in Parallel and Cluster SystemsSpringer Science & Business Media, 2003. gada 30. jūn. - 251 lappuses Multiple processor systems are an important class of parallel systems. Over the years, several architectures have been proposed to build such systems to satisfy the requirements of high performance computing. These architectures span a wide variety of system types. At the low end of the spectrum, we can build a small, shared-memory parallel system with tens of processors. These systems typically use a bus to interconnect the processors and memory. Such systems, for example, are becoming commonplace in high-performance graph ics workstations. These systems are called uniform memory access (UMA) multiprocessors because they provide uniform access of memory to all pro cessors. These systems provide a single address space, which is preferred by programmers. This architecture, however, cannot be extended even to medium systems with hundreds of processors due to bus bandwidth limitations. To scale systems to medium range i. e. , to hundreds of processors, non-bus interconnection networks have been proposed. These systems, for example, use a multistage dynamic interconnection network. Such systems also provide global, shared memory like the UMA systems. However, they introduce local and remote memories, which lead to non-uniform memory access (NUMA) architecture. Distributed-memory architecture is used for systems with thousands of pro cessors. These systems differ from the shared-memory architectures in that there is no globally accessible shared memory. Instead, they use message pass ing to facilitate communication among the processors. As a result, they do not provide single address space. |
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INTRODUCTION | 3 |
12 Parallel Architectures | 4 |
121 SIMD Systems | 5 |
122 MIMD Systems | 6 |
13 Job Scheduling | 8 |
14 Software Architectures | 10 |
15 Overview of the Monograph | 11 |
PARALLEL AND CLUSTER SYSTEMS | 13 |
53 Performance of Task Scheduling Policies | 126 |
532 Results and Discussion | 131 |
5321 Principal Comparison | 132 |
5322 Impact of Variance in Task Service Time | 133 |
5323 Impact of Variance in Task Distribution | 134 |
5324 Effect of Window Size | 135 |
5325 Sensitivity to Other Parameters | 137 |
54 Conclusions | 138 |
22 Parallel Architectures | 15 |
222 NUMA Systems | 16 |
224 Distributed Shared Memory | 18 |
23 Example Parallel Systems | 19 |
232 Stanford DASH System | 21 |
233 ASCI Systems | 22 |
24 Interconnection Networks | 24 |
241 Dynamic Interconnection Networks | 26 |
242 Static Interconnection Networks | 29 |
25 Interprocess Communication | 36 |
252 MPI | 40 |
253 TreadMarks | 43 |
26 Cluster Systems | 45 |
261 Beowulf | 46 |
27 Summary | 48 |
PARALLEL JOB SCHEDULING | 49 |
32 Parallel Program Structures | 51 |
322 DivideandConquer Programs | 52 |
323 Matrix Factorization Programs | 53 |
33 Task Queue Organizations | 55 |
3311 Improving Centralized Organization | 57 |
3312 Improving Distributed Organization | 59 |
34 Scheduling Policies | 63 |
3412 Dynamic Policies | 64 |
342 An Example SpaceSharing Policy | 65 |
3421 Adaptive SpaceSharing Policy | 66 |
3422 A Modification | 67 |
3424 Performance Comparison | 68 |
3425 Performance Comparison | 69 |
3426 Handling Heterogeneity | 75 |
343 TimeSharing Policies | 78 |
344 Hybrid Policies | 80 |
35 Example Policies | 81 |
352 ASCI BluePacific | 82 |
353 Portable Batch System | 83 |
36 Summary | 84 |
HIERARCHICAL TASK QUEUE ORGANIZATION | 85 |
HIERARCHICAL TASK QUEUE ORGANIZATION | 87 |
42 Hierarchical Organization2 | 89 |
43 Workload and System Models | 93 |
44 Performance Analysis | 96 |
442 Utilization Analysis | 97 |
4421 Centralized Organization | 98 |
4423 Hierarchical Organization | 99 |
4432 Distributed Organization | 100 |
45 Performance Comparison | 101 |
451 Impact of Access Contention | 102 |
452 Effect of Number of Tasks | 104 |
453 Sensitivity to Service Time Variance | 107 |
454 Impact of System Size | 109 |
455 Influence of Branching and Transfer Factors | 111 |
46 Performance of Dynamic Task Removal Policies | 114 |
47 Summary | 117 |
PERFORMANCE OF SCHEDULING POLICIES | 121 |
52 Performance of Job Scheduling Policies | 122 |
522 Results | 123 |
5222 Sensitivity to Task Service Time Variance | 124 |
5223 Sensitivity to Variance in Task Distribution | 125 |
PERFORMANCE WITH SYNCHRONIZATION WORKLOADS | 141 |
62 Related Work | 142 |
63 System and Workload Models | 145 |
64 Spinning and Blocking Policies | 147 |
642 Blocking Policies | 148 |
651 Workload Model | 149 |
6521 Principal Comparison | 150 |
6522 Sensitivity to Service Time Variance | 153 |
6523 Impact of Granularity | 154 |
6524 Impact of Queue Access Time | 155 |
66 Barrier Synchronization Workload Results | 156 |
662 Simulation Results | 157 |
6622 Sensitivity to Service Time Variance | 160 |
6624 Impact of Queue Access Time | 161 |
67 Cache Effects | 162 |
68 Summary | 163 |
HIERARCHICAL SCHEDULING POLICIES | 165 |
SCHEDULING IN SHAREDMEMORY MULTIPROCESSORS | 167 |
72 SpaceSharing and TimeSharing Policies2 | 168 |
722 Modified RRJob | 170 |
74 Performance Evaluation | 174 |
742 Performance Analysis | 176 |
7421 Effect of Scheduling Overhead | 178 |
7422 Impact of Variance in Service Demand | 181 |
7423 Effect of Task Granularity | 183 |
7424 Effect of the ERF Factor | 184 |
7425 Effect of Quantum Size | 186 |
75 Performance with Lock Accessing Workload | 187 |
752 Results | 188 |
76 Conclusions | 190 |
SCHEDULING IN DISTRIBUTEDMEMORY MULTICOMPUTERS | 193 |
82 Hierarchical Scheduling Policy2 | 195 |
83 Scheduling Policies for Performance Comparison | 200 |
84 Workload Model | 201 |
85 Performance Comparison | 203 |
852 Performance with NonUniform Workload | 204 |
8521 Performance with 5050 distribution | 205 |
8522 Sensitivity to variance in job service demand | 206 |
8523 Performance under 5025 distribution | 208 |
8524 Performance under 5075 distribution | 209 |
853 Discussion | 210 |
86 Conclusions | 211 |
SCHEDULING IN CLUSTER SYSTEMS | 213 |
92 Hierarchical Scheduling Policy | 215 |
921 Job Placement Policy | 216 |
922 Dynamic Load Balancing Algorithm | 218 |
93 SpaceSharing and TimeSharing Policies | 220 |
931 SpaceSharing Policy | 221 |
94 Performance Comparison | 222 |
941 Workload Model | 224 |
943 NonUniform Workload Results | 227 |
95 Summary | 229 |
EPILOG | 231 |
CONCLUSIONS | 233 |
102 Concluding Remarks | 236 |
239 | |
249 | |
Citi izdevumi - Skatīt visu
Hierarchical Scheduling in Parallel and Cluster Systems Sivarama Dandamudi Ierobežota priekšskatīšana - 2012 |
Hierarchical Scheduling in Parallel and Cluster Systems Sivarama Dandamudi Priekšskatījums nav pieejams - 2012 |
Bieži izmantoti vārdi un frāzes
algorithm architecture blocking policies bottleneck branching factor cache Cdin centralized organization cluster systems coefficient of variation context switch context switch overhead Cray T3E distributed organization distributed-memory systems example execution exponentially distributed FCFS policy hierarchical organization hierarchical policy hierarchical scheduling policy hierarchical task queue hypercube impact implement increases interconnection network job scheduling job scheduling policies job structure load sharing lock accessing workload MAP policy Mean response memory MIMD multiprocessor multiprogramming node number of iterations number of processors number of queue number of tasks parallel systems parameter parent queue partition Performance sensitivity problem quantum root queue round robin policies RR3 policy RRJob service time CV service time variance shared-memory systems shown in Figure SIMD space-sharing policy spinning policy system load system utilization task queue organization task scheduling policies task transfer three policies time-sharing policies transfer factor values Wavg workload model workstations wormhole routing
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