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I/O Streaming Evaluation of Batch
Queries for Data-Intensive
Computational Turbulence
Kalin Kanov, Eric Perlman, Randal Burns, Yanif Ahmad, and Alexander Szalay
Johns Hopkins University
I/O Streaming For Batch Queries
 Based on partial sums
 Allows access to the underlying data in any order and in parts
 Data streamed from disk in a single pass
 Eliminates redundant I/O
 Over an order of magnitude improvement in performance
over direct evaluation of queries
Introduction
 Data-intensive computing breakthroughs have allowed for
new interaction with scientific numerical simulations
 Formerly, analysis performed during the computation
 No data stored for subsequent examination
 Turbulence Database Cluster
 Stores entire space-time evolution of the simulation
 Two datasets totaling 70TB; part of the 1.1PB GrayWulf cluster
 Provides public access to world-class simulation
 Implements “immersive turbulence*” approach
*E. Perlman, R. Burns,Y. Li, and
C. Meneveau. Data exploration of turbulence
simulations using a database cluster. In Supercomputing, 2007.
Turbulence Database Cluster
Motivation
 Without I/O streaming:
 Heavy DB usage slows down the service by a factor of 10 to 20
 Query evaluation techniques adapted from simulation code do
not access data coherently
 Substantial storage overhead (~42%) incurred to localize each
computation
 Turbulence queries:
 95% of queries perform Lagrange Polynomial interpolation
 Can be evaluated in parts
Processing a Batch Query
0
10
11
14
15
8
9
12
13
2
3
6
7
0
1
4
5
1
2
3
4
5
6
7
8
9
1
0
1
1
1
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1
3
1
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1
5
Processing a Batch Query
query 2
10
0
11
14
15
8
9
12
13
2
3
6
7
0
1
4
query 1
5
1
q1: 0
2
3
5
6
4
6
q2: 9
2
1
2
3
1
4
q3: 4
5
6
7
 Multiple disk seeks
query 3
4
1
1
1
 Redundant I/O
7
8
8
9
9
1
2
1
0
1
1
1
2
1
3
1
4
1
5
Streaming Evaluation Method
 Linear data requirements of the computation allow for:
 Incremental evaluation
 Streaming over the data
 Concurrent evaluation of batch queries
Processing a Batch Query
query 2
10
0
11
14
15
8
9
12
13
2
3
6
7
0
1
4
query 1
5
1
2
I/O Streaming: 0
q1
3
 Sequential I/O
 Single pass
query 3
4
5
6
7
8
1
0
9
1
1
1
2
3
4
5
6
7
8
9
1
1
q1
q1
q1
q1
q3
q1
q3
q1
q1
q2
q3
q3
q2
1
2
1
2
q1
q2
1
3
1
4
q2
1
4
1
5
Lagrange Polynomial Interpolation
N
f (x', y')   ly
j1
p
N
j
2
N
(y') lx
n
N
i
2
(x')  f (x
i1
Lagrange coefficients

n
N
i
2
,y
Data
p
N
j
2
)
Processing a Batch Query
 Input queries pre-processed into a key-value dictionary
 Keys are z-index values of data atoms stored in DB
 Entries are lists of queries
 Temp table is created out of dictionary keys
 Execute a join between temp table and data table
 When data atom is read-in all queries that need data
from it are processed and their partial sums updated
Experimental Evaluation
 Random workloads:
 across the entire cube space
 a 1283 subset of the entire space
 Workload from the usage log of the Turbulence cluster
 Compare with direct methods of evaluation:
 Direct
 Sorting
 Join/Order By
3D Workload
 Used for generating
global statistics
128 Workload
 Used for:
 Examining ROI
 Creating visualizations
Experimental Evaluation
 Random workloads:
 across the entire cube space
 a 1283 subset of the entire space
 Workload from the usage log of the Turbulence cluster
 Compare with direct methods of evaluation:
 Direct
 Sorting
 Join/Order By
Setup
 Experimental version of the MHD database
 ~300 timesteps of the velocity fields of the MHD simulation
 Two 2.33 GHz dual quad-core Windows 2003 servers with SQL
Server 2008 and 8GB of memory
 Part of the 1.1PB GrayWulf cluster with aggregate low-level
throughput of 70 GB/sec
 Data tables striped across 7 disks per node
3D Workload
 I/O Streaming
 Join/Order
Sorting
Bytoof
executes
a magnitude
more sequential
entire
batch
acces
as a join
Over
anleads
order
improvement
 Each atom is read only once
 Effective cache usage
128 Workload
 Less I/O
 More data sharing
 I/O Streaming alleviates I/O bottleneck
 Computation emerges as the more costly operation
128 Workload
Future Work
 Extend I/O streaming technique to other decomposable
kernel computations:
 Differentiation
 Temporal interpolation
 Filtering
 Multi-job batch scheduling:
 Integrate into a batch scheduling framework such as JAWS*
*X. Wang, E. Perlman, R. Burns, T. Malik, T. Budavari, C. Meneveau, and A. Szalay. Jaws: Job-aware
workload scheduling for the exploration of turbulence simulations. In Supercomputing, 2010.
Summary
 I/O Streaming method for data-intensive batch queries
 Single pass by means of partial-sums
 Effective exploitation of data sharing
 Improved cache locality
 Over an order of magnitude improvement in performance
Questions
Images courtesy of Kai Buerger ([email protected])