department of computer science mapreduce for the cell b. e. architecture marc de kruijf university...

Department of Computer ScienceDepartment of Computer Science

MapReduce for the Cell B. E. Architecture

Marc de KruijfUniversity of Wisconsin−Madison

Advised by Professor Sankaralingam

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MapReduce A model for parallel programming Proposed by Google

Large scale distributed systems – 1,000 node clusters

Applications: Distributed sort Distributed grep Indexing

Simple, high-level interface Runtime handles:

parallelization, scheduling, synchronization, and communication

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Cell B. E. Architecture A heterogeneous

computing platform: 1 PPE, 8 SPEs

Programming is hard Multi-threading is

explicit SPE local memories

are software-managed

The Cell is like a “cluster-on-a-chip”

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MotivationMapReduce

Scalable parallel modelSimple interface

Cell B. E.Complex parallel

architectureHard to program

MapReduce for the Cell B.E. Architecture

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Overview Motivation

MapReduce Cell B.E. Architecture

MapReduce Example Design Evaluation

Workload Characterization Application Performance

Conclusions and Future Work

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MapReduce ExampleCounting word occurrences in a set of documents:

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Overview Motivation





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Design

Flow of Execution

Five stages: Map, Partition, Quick-sort, Merge-sort, Reduce

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Design

Flow of Execution


1. Map streams key/value pairs

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Design

Flow of Execution



Key grouping implemented as:

2. Partition – hash and distribute

3. Quick-sort 4. Merge-sort

two-phase external sort

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Design

Flow of Execution







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Design

Flow of Execution







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DesignFlow of Execution






5. Reduce “reduces”key/list-of-values pairs tokey/value pairs.


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Overview Motivation





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Evaluation Methodology MapReduce Model Characterization

Synthetic micro-benchmark with six parameters

Run on a 3.2 GHz Cell Blade Measured effect of each parameter on execution time

Application Performance Comparison Six full applications

MapReduce versions run on 3.2 GHz Cell Blade Single-threaded versions run on 2.4 GHz Core 2 Duo

Evaluation Measured speedup comparing execution times Measured overheads on the Cell monitoring SPE idle

time Measured ideal speedup assuming no Cell overheads

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MapReduce Model Characterization

Model CharacteristicsCharacter

isticDescription

Map intensity Execution cycles per input byte to Map

Reduce intensity

Execution cycles per input byte to Reduce

Map fan-out Ratio of input size to output size in Map

Reduce fan-in Number of values per key in Reduce

Partitions Number of partitions

Input size Input size in bytes

Effect on Execution Time

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Application Performance Applications

histogram: counts bitmap RGB occurrences

kmeans: clustering algorithmlinearReg: least-squares linear

regressionwordCount: word countNAS_EP: EP benchmark from NAS

suitedistSort: distributed sort

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Speedup Over Core 2 Duo

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Runtime Overheads

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Overview Motivation





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Conclusions Programmability benefits High-performance on computationally

intensive workloads Not applicable to all application types

Future Work Additional performance tuning Extend for clusters of Cell processors

Hierarchical MapReduce


Questions?


Backup Slides

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MapReduce API

void MapReduce_exec(MapReduce Specification specification);

The exec function initializes the MapReduce runtime and executes MapReduce according to the user specification.

void MapReduce_emitIntermediate(void **key, void **value);void MapReduce_emit(void **value);

These two functions are called by the user-defined Map and Reduce functions, respectively. These functions take references to pointers as arguments, and modify the referenced pointer to point to pre-allocated storage. It is then the responsibility of the application to provision this storage.

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Optimizations1) Priority work queue

Distributes load Avoids serialization

Pipelined execution maximizes concurrency

2) Double-buffering3) Application support

Map only Map with sorted

output Chaining invocations

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Optimizations1) Priority work queue

Distributes load Avoids serialization

Pipelined execution maximizes concurrency

2) Double-buffering3) Application support

Map only Map with sorted

output Chaining invocations

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Optimizations4) Balanced merge (n / log(n) better bandwidth utilization as n

→ ∞)

5) Map and Reduce output regions pre-allocated. optimal memory alignment bulk memory transfers no user memory management no dynamic allocation overhead

department of computer science mapreduce for the cell b. e. architecture marc de kruijf university...

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