Synthetic Traffic Generation: a Tool for Dynamic Interconnect Evaluation

Transcription

1 Synthetic Traffic Generation: a Tool for Dynamic Interconnect Evaluation W. Heirman, J. Dambre, J. Van Campenhout ELIS Department, Ghent University, Belgium Sponsored by IAP-V PHOTON & IAP-VI photonics@be, Belgian Science Policy Office PHOTONnetwork

2 Outline Introduction Synthetic traffic generation Results Conclusions 2

3 Distributed shared-memory architecture Network is part of the memory hierarchy instruction: 0.5 ns cache: 5 ns supercomputer CPU MEM cache DDR: 50 ns CPU MEM network: 500 ns CPU MEM CPU MEM CPU MEM on-chip CPU MEM CPU MEM CPU MEM server 3

4 Interconnect requirements load Link #5 Non-uniform network traffic in space and time time Link #9 load time Link #13 load time => Reconfigurable network? 4

5 Reconfiguration implementation: base network + extra reconfigurable links other dynamic networks : e.g. per-link voltage scaling Broadcast element Processor nodes Tunable lasers Photodetectors CPU 1 CPU 1 CPU 2 CPU Base network (fixed) Extra links (reconfigurable) CPU n Fiber links CPU n 5

6 Evaluate networks with synthetic traffic Mimics the behavior of real traffic But without the computational cost of modeling application, OS, CPUs, caches, Application OS CPU Caches Reconfigurable network simulator simulation time 10 network traffic Synthetic traffic generator Reconfigurable network simulator 6

7 We need better synthetic traffic Reconfiguration exploits low-frequency dynamics in the network traffic Trace-driven simulation using static traffic patterns (uniform, hotspot, shuffle, ) won t do! Full execution-driven simulation (traffic is driven by application: FFT, weather forecast, database) is too slow! 7

8 Outline Introduction Synthetic traffic generation Results Conclusions 8

9 Realistic synthetic traffic generation One execution-driven simulation Resulting traffic profile re-used many times Application OS CPU Caches Parameter extraction network traffic 1 n Statistical traffic profile Synthetic traffic generator Reconfigurable network 9

10 Preserve packet-interdependencies by using packet groups owner sharer... sharer WBreq (2) WBreply (3) (3) (2) home home INVreq (2) home INVreply (3) REQ (1) REPLY (2) REQ (1) REPLY (4) REQ (1) REPLY (4) processor processor processor Packets are processed/generated in groups, corresponding to one memory operation each 10

11 Distribution of # involved nodes n 11

12 Reuse distance of home nodes: introduce locality next destination node A previous destinations 2 node C node B node A node D 12

13 Computation or think time Models time delay between subsequent requests 13

14 Outline Introduction Synthetic traffic generation Results Conclusions 14

15 Simulations Simulation platform: Simics, providing functional multiprocessor simulation 16 UltraSPARC III processors SPLASH-2 parallel benchmarks Timing model: Computes the latency for each memory access Models caches, interconnection network Base network: 4x4 torus Extra links: configurable number, fan-out, reconfiguration interval 15

16 Simulations Once per benchmark: Simulate execution of the benchmark, base network only, measuring traffic profile (1) For each set of extra link parameters: Execution-driven simulation with reconfigurable network (2) correct result Trace-driven simulation using (simplified) traffic from (2) tracing error Trace-driven simulation using (simplified) traffic from (1) traffic-dependence on network Trace-driven simulation using synthetic traffic total error 16

17 Several parameters can be measured 17

18 Detailed view of average packet latency 18

19 Variability for shorter traces synthetic traffic execution-driven trace-driven +profiling* exec-driven * assuming traffic profile is re-used 100 times 19

20 Outline Introduction Synthetic traffic generation Results Conclusions 20

21 Conclusions Synthetic traffic generation was extended to shared-memory cache-coherence protocols, reconfigurable networks Good relative accuracy for different network topologies Much less computationally expensive (x10), even more so for shorter traces (x100) Reproducibility equal to or better than execution-driven simulations 21