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Bottleneck Elimination from Stream Graphs S. M. Farhad The University of Sydney Joint work with Yousun Ko Bernd Burgstaller Bernhard Scholz

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2 Outline Motivation Multicore Stream programming Research question Our work Summary 2

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3 Multicores Are Here! 1985199019801970197519952000 4004 8008 80868080286386486PentiumP2P3 P4 Itanium Itanium 2 200520?? # of cores 1 2 4 8 16 32 64 128 256 512 Athlon Raw Power4 Opteron Power6 Niagara Yonah PExtreme Tanglewood Cell Intel Tflops Xbox360 Cavium Octeon Raza XLR PA-8800 Cisco CSR-1 Picochip PC102 Broadcom 1480 Opteron 4P Xeon MP Ambric AM2045

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4 Multicores Are Here! Uniprocessors: C is the common machine language 1985199019801970197519952000 4004 8008 80868080286386486PentiumP2P3 P4 Itanium Itanium 2 2005 Raw Power4 Opteron Power6 Niagara Yonah PExtreme Tanglewood Cell Intel Tflops Xbox360 Cavium Octeon Raza XLR PA-8800 Cisco CSR-1 Picochip PC102 Broadcom 1480 20?? # of cores 1 2 4 8 16 32 64 128 256 512 Opteron 4P Xeon MP Athlon Ambric AM2045

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5 Multicores Are Here! What is the common machine language for multicores? 1985199019801970197519952000 4004 8008 80868080286386486PentiumP2P3 P4 Itanium Itanium 2 2005 Raw Power4 Opteron Power6 Niagara Yonah PExtreme Tanglewood Cell Intel Tflops Xbox360 Cavium Octeon Raza XLR PA-8800 Cisco CSR-1 Picochip PC102 Broadcom 1480 20?? # of cores 1 2 4 8 16 32 64 128 256 512 Opteron 4P Xeon MP Athlon Ambric AM2045

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6 Stream Programming Paradigm Research topic in parallel programming Various forms of parallelism Pipeline, task, and data Applications Signal Processing Multi-media High-Performance Computing Programs expressed as stream graphs Streams Infinite sequence of data elements (aka. Tokens) Actor Functions applied to streams 6 Actor Stream

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7 Properties of Stream Program Regular and repeating computation Independent actors with explicit communication Producer / Consumer dependencies 7 Adder Speaker AtoD FMDemod LPF 1 Splitter Joiner LPF 2 LPF 3 HPF 1 HPF 2 HPF 3

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8 StreamIt Language [ASPLOS’2&6, PLDI’3] An implementation of stream prog. Each construct has single input/output stream Hierarchical structure Filters can be stateful/stateless parallel computation may be any StreamIt language construct joiner splitter pipeline feedback loop joiner splitter splitjoin filter 8

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9 Research Question: How to Eliminate Bottlenecks (Hot Actors) from Stream Graphs? 9

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10 Mapping Actors Core 1 B 60 C D 5 5 A Core 2Core 3 5 A D 5 B 60 C T =10sT = 60s Make span = 60s, Speedup = 130/60 = 2.17 10

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11 Bottleneck Actors Limit the Performance B 60 C D 5 5 A D 5 5 A B_1 20 2 s1 2 j1 B_2 20 B_3 20 C_1 20 2 s2 2 j2 C_2 20 C_3 20 Hot actor duplication Core 1 Core 2 Core 3 5 A s1 2 B_1 20 C_1 20 B_2 20 j1 2 s2 2 C_2 20 j2 2 B_3 20 C_3 20 D 5 T = 47sT = 46sT = 45s Make span = 47s, Speedup = 130/47 = 2.77 11

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12 Bottleneck Resolving of Stream Program Contd. Current state of the art Integer Linear Programming Intractable How to find a fast and good solution? Heuristics Optimal 12

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Our Work A data rate transfer model to detect and eliminate bottlenecks We separate the bottleneck elimination from the actor allocation Heuristics to solve bottleneck problem efficiently 13

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14 Our Data Transfer Model Throughput depends on the data rate of the actors (maximize) Data transfer model forms a system of sim. functional linear equation Compute a closed form of the output data rate We also consider a processor utilization function for each actor ABC 1511 z 14

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15 Bottleneck Analysis The throughput is limited by Processor capacity of the cores Memory bandwidth A quantitative analysis determines An upper bound of the throughput imposed by an actor An upper bound of the throughput imposed by the parallel system Hot actor Upper bound (actor) < upper bound (system) 15

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h2h2 h1h1 Hot Region Maximal connected subgraph where and each is hot and stateless 16 B C D A E F G

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17 Resolving Bottleneck Options 17 B 60 C D 5 5 A D 5 5 A B_1 20 2 s1 B_2 20 B_3 20 C_1 20 2 j1 C_2 20 C_3 20 Hot region duplication D 5 5 A B_1 20 2 s1 2 j1 B_2 20 B_3 20 C_1 20 2 s2 2 j2 C_2 20 C_3 20 Hot actor duplication

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18 Region Duplication further Increases Performance 18 D 5 5 A B_1 20 2 s1 B_2 20 B_3 20 C_1 20 2 j1 C_2 20 C_3 20 Core 1 5 A B_1 20 C_1 20 Core 2 s1 2 B_2 20 j1 2 C_2 20 Core 3 B_3 20 C_3 20 D 5 T = 45sT = 44sT = 45s Make span = 45s, Speedup = 130/45 = 2.89 Mapping

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Cascading Effect of Duplication Actors may become hot due to duplication of other actors B C D A E A B_1 s1 B_2B_3 C_1 j1 C_2C_3 E D

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d B =2d h1 =2 d D =3 d E =2 d F =3 d h2 =3 Duplication Factor of an Actor and a Hot Region The # of times the actor needs to be duplicated Maximum duplication factor of the actors of the hot region 20 B C D A E F G

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Heuristics to Resolve Bottlenecks 21 Determine hot regions Determine duplication factors of hot regions Duplicate hot regions Optimal solution? Experiment

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22 Summary A simple quantitative analysis to detect and eliminate bottlenecks We separate the bottleneck elimination from the actor allocation Heuristics to eliminate bottlenecks 22

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23 Related Works [1] Static Scheduling of SDF Programs for DSP [Lee ‘87] [2] StreamIt: A language for streaming applications [Thies ‘02] [3] Phased Scheduling of Stream Programs [Thies ’03] [4] Exploiting Coarse Grained Task, Data, and Pipeline Parallelism in Stream Programs [Thies ‘06] [5] Orchestrating the Execution of Stream Programs on Cell [Scott ’08] [6] Software Pipelined Execution of Stream Programs on GPUs [Udupa‘09] [7] Synergistic Execution of Stream Programs on Multicores with Accelerators [Udupa ‘09] 23

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Orchestration by Approximation Mapping Stream Programs onto Multicore Architectures S. M. Farhad (University of Sydney) Joint work with Yousun Ko Bernd.

Orchestration by Approximation Mapping Stream Programs onto Multicore Architectures S. M. Farhad (University of Sydney) Joint work with Yousun Ko Bernd.

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