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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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