PDAC-10

Middleware Solutions for Data-Intensive (Scientific) Computing on

Clouds

Gagan Agrawal

Ohio State University

(Joint Work with Tekin Bicer, David Chiu, Yu Su, ..)

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PDAC-10

Motivation

• Cloud Resources• Pay-as-you-go• Elasticity • Black boxes from a performance view-point

• Scientific Data – Specialized formats, like NetCDF, HDF5, etc.

– Very Large Scale

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Ongoing Work at Ohio State

• MATE-EC2: Middleware for Data-Intensive Computing on EC2 – Alternative to Amazon Elastic MapReduce

• Data Management Solutions for Scientific Datasets– Target NetCDF and HDF5

• Accelerating Data Mining Computations Using Accelerators • Resource Allocation Problems on Clouds

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MATE-EC2: Motivation

• MATE – MapReduce with an Alternate API • MATE-EC2: Implementation for AWS Environments• Cloud resources are blackboxes• Need for services and tools that can…

– get the most out of cloud resources– help their users with easy APIs

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MATE vs. Map-Reduce Processing Structure

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• Reduction Object represents the intermediate state of the execution• Reduce func. is commutative and associative• Sorting, grouping.. overheads are eliminated with red. func/obj.

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MATE-EC2 Design

• Data organization– Three levels: Buckets, Chunks and Units

– Metadata information

• Chunk Retrieval– Threaded Data Retrieval

– Selective Job Assignment

• Load Balancing and handling heterogeneity– Pooling mechanism

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MATE-EC2 Processing Flow

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C0

C5

Cn

Computing LayerJob Scheduler Metadata File

Request Job from Master NodeC0 is assigned as jobRetrieve chunk pieces andWrite them into the buffer

T0 T

1T

2 T3

Pass retrieved chunk to Computing Layer and processRequest another jobC5 is assigned as a jobRetrieve the new job

EC2 Slave Node

S3 Data Object

EC2 Master Node

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Experiments• Goals:

– Finding the most suitable setting for AWS – Performance of MATE-EC2 on heterogeneous and

homogeneous environments– Performance comparison of MATE-EC2 and Map-

Reduce• Applications: KMeans and PCA• Used Resources:

– 4 Large EC2 instances for processing, 1 Large instance for Master– 16 Data objects on S3 (8.2GB total data set for both app.)

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Diff. Data Chunk Sizes

• KMeans• 16 Retrieval threads• Performance

increase– 8M vs. others

• 1.13 to 1.30

– 1 Thread vs. 16 Threads versions

• 1.24 to 1.81

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Diff. Number of Threads

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• 128MB chunk size• Performance

increase in Fig. (KMeans)– 1.37 to 1.90

• Performance increase for PCA– 1.38 to 1.71

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Selective Job Assignment

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• Performance increase in Fig. (KMeans)– 1.01 to 1.14

• For PCA– 1.19 to 1.68

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Heterogeneous Env.

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• L: Large instances S: Small instances

• 128MB chunk size• Overheads in Fig.

(KMeans)– Under 1%

• Overheads for PCA– 1.1 to 11.7

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MATE-EC2 vs. Map-Reduce

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• Scalability (MATE)– Efficiency: 90%

• Scalability (MR)– Efficiency: 74%

• Speedups: – MATE vs. MR

• 3.54 to 4.58

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MATE-EC2: Continuing Directions

• Cloud Bursting– Cloud as an Complement or On-Demand Alternative

to Local Resources

• Autotuning for a New Cloud Environment – Data Storage can be black-box

• Data-Intensive Applications on Cluster of GPUs– Programming Model, System Design

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Outline

• MATE-EC2: Middleware for Data-Intensive Computing on EC2 – Alternative to Amazon Elastic MapReduce

• Data Management Solutions for Scientific Datasets– Target NetCDF and HDF5

• Accelerating Data Mining Computations Using Accelerators • Resource Allocation Problems on Clouds

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Data Management: Motivation

• Datasets are becoming extremely large• Scientific datasets are in formats like NetCDF and

HDF5• Existing database solutions are not scalable

– Can’t help with native data formats

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Data Management: Use Scenarios

• Data Dissemination Efforts– Support User-Defined Subsetting and Data Aggregation

• Implementing Data Processing Applications– Higher-level API than NetCDF/HDF5 libraries

• Visualization Tools (ParaView etc.) – Data format Conversion on Large Datasets

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Initial Prototype: Data Subsetting With Relational View on NetCDF

Parse the SQL expression

Metadata for netcdf dataset

Generate data access code

Filter variable value

Filter dimensions

Partition tasks and assign to slave processes

Execute query

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Metadata descriptor

• Dataset Storage Description– List the nodes and the directories where the data is

resident.

• Dataset Layout Description– Header part of each netcdf file

• Naturally included in netcdf dataset• Save the energy for generating the metadata

– Describe the layout of each netcdf file

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Pre-filter and Post-filter

• Pre-filter: – Take SQL grammar and metadata as input

– Do filtering based on dimensions of variable

– Support both direct dimensions and coordinate variable

• Post-filer:– Do filtering based on variable value

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Query Partition

• Partition current query into several sub-queries and assign each sub-query to a slave process.

• Two partition criteria– Consider the continuous of the memory

– Consider data aggregation(future)

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Experiment Setup

• Application: – Global Cloud Resolving Model and Data (GCRM)

• Environment: – Glenn System in Ohio Supercomputer Center

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SQL queries

No. Description Percent

SQL 1 SELECT pressure FROM dataset; 100%

SQL 2 SELECT pressure FROM dataset WHERE cells<=20481

50%

SQL 3 SELECT pressure FROM dataset WHERE cells>20481 AND layers>330;

25%

SQL 4 SELECT pressure FROM dataset WHERE cells<=20481 AND layers<250;

10%

SQL 5 SELECT pressure FROM dataset WHERE cells <= 20481 AND time<=781710 AND layers<250;

1%

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Scalability with different data size

• 8 processes

• Execution time scaled almost linearly within each query

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Time improvement for using pre-filter

• 4 processes;

• SQL5 (only query 1% of the data);

• Prefilter efficiently decreases the query size, improve the performance.

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Scalability with Increasing No. of Sources

• 4G dataset;

• SQL1 (full scan of the data table);

• Execution time scaled almost linearly

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Data Management: Continuing Work

• Similar Prototype with HDF5 under Implementation• Consider processing, not just

subsetting/aggregation– Map-Reduce like Processing for NetCDF/HDF5

datasets?

• Consider Format Conversion for Existing Tools

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Outline

• MATE-EC2: Middleware for Data-Intensive Computing on EC2 – Alternative to Amazon Elastic MapReduce

• Data Management Solutions for Scientific Datasets– Target NetCDF and HDF5

• Accelerating Data Mining Computations • Resource Allocation Problems on Clouds

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PDAC-10 29

User Input:Simple C code with

annotations

Application Developer

Multi-core Middlewar

e API

GPU Code for CUDA

Compilation Phase

Code Generator

Run-time System

Worker Thread Creation and Management

Map Computation to CPU and GPU

Dynamic Work Distribution

System for Mapping to Heterogeneous Configurations

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K-Means on GPU + Multi-Core CPUs

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Summary

• Dataset Sizes are Increasing • Clouds add many challenges • Many challenges in data processing on clouds

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