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Reducing Server Data Traffic Using a Hierarchical Computation Model

Juan Rubio, Lizy Kurian John,IEEE Transactions on Parallel and Distributed System

VOL16, NO. 10, October 2005Presented by 張肇烜

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Outline

Introduction Hierarchical Computing Evaluation Methodology Results Conclusions

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Introduction

Commercial workloads impose heavy demands on memory and storage subsystems in a server and often result in a large amount of traffic in I/O and memory buses.

The goal:We propose a hierarchical computing (HC) reduce the

data movement between the storage subsystem and the processing units.

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Introduction (cont.)

In the HC model, computing is distributed across a computer system’s memory/storage hierarchy.

As we show in this paper, several operations may be efficiently performed where the data resides or at an intermediate level of the hierarchy.

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Introduction (cont.)

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Hierarchical Computing

To take advantage of the parallelism present in the queries, the system decomposes the database queries into simpler operations.

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Hierarchical Computing (cont.)

Organization

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Hierarchical Computing (cont.)

Execution ModelThe first step is the decomposition of the query

into simpler operations.Then, the order of execution of the operations is

decided.Once the operations are scheduled, they are

ready to be executed.HC system differs from a conventional system.

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Hierarchical Computing (cont.)

Execution of an operation in the hierarchical computing model.

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To facilitate communication of the data, the hardware provides basic control flow signals.

All commands are tagged by the originating node with a unique identifier.

The responding node also tags the response based on the tag of the initial command and the ID of the node.

Finally, the model implements a namespace locator in the form of a software-managed table allocation index.

Hierarchical Computing (cont.)

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The current programming interface allows an implementation of the most common operations:Selection: locates records within a single table

that match a particular criterion.Join: merges the results of two or more

selections or tables.Sort query results by some criterionInsert, remove, and update records from an

existing table.

Hierarchical Computing (cont.)

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Hierarchical Computing (cont.)

The current programming interface allows an implementation of the most common operations:Selection: locates records within a single table

that match a particular criterion.Join: merges the results of two or more

selections or tables.Sort query results by some criterionInsert, remove, and update records from an

existing table.

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Hierarchical Computing (cont.)

Individual primitive:

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Hierarchical Computing (cont.)

Aggregate primitive:

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Coherence:If the routine running in one processor accesses

data and the system instructs another processor to regain write access of that data.

Security:HC system is allowed by having metadata that

is kept in the same storage module as the data.

Hierarchical Computing (cont.)

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Evaluation Methodology

Symmetric Multiprocessing (SMP) :a computer architecture that provides fast perfor

mance by making multiple CPUs available to complete individual processes simultaneously

Cache-Coherent Non-Uniform Memory Access (CC-NUMA) :The system runs one operating system and sho

ws only a single memory image to the user eventhough the memory is physically distributed over the processors.

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Evaluation Methodology (cont.)

Configuration of the base systems:

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Evaluation Methodology (cont.)

2X4 CC-NUMA:

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Evaluation Methodology (cont.)

HC configurations:

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Evaluation Methodology (cont.)

Dimensions of the Tables for Our Implem-entation of a TPC-H-Like Workload in DB2:

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Evaluation Methodology (cont.)

Description of selected TPC-H-Like queries:

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Results

Amount of data transferred over the global interconnect for selected queries.

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Results (cont.)

Average number of processors that are waiting for a global interconnect every cycle.

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Results (cont.)

Average memory access time.

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Results (cont.)

Speedups of hierarchical computing systems over base shared memory multiprocessor system with a similar amount of computation and storage resources.

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Results (cont.)

Speedups of large hierarchical computing systems over base multiprocessor systems with a similar amount of computation, storage, and communication resources.

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Conclusions

We believe hat hierarchical computing, which exploits parallelism, distributes computations, and reduces data transport requirements, is a desirable model of computation for future server systems.