Biem, A

Elmegreen, B

Ensor, A

Gulyaev, S

Mahmoud, M

AUT Scholarly Commons

Mahmoud S. Mahmoud1, Andrew Ensor1, Alain Biem2, Bruce Elmegreen2 and Sergei Gulyaev1
1 Institute for Radio Astronomy & Space Research (IRASR), AUT University, NZ. 2 IBM omas J. Watson Research Center, Yorktown Heights, NY.
An InfoSphere Streams Based Approach for Implementing an
FX-Style Auto-Correlation Spectrometer with Accelerator Support
e signicantly reduced data is 
persisted to a suitable store by this 
SINK PE. e persisted data can be 
used to perform longer integrations 
or deeper spectral analysis.
UDOP (User Dened OPerator) 
PEs are used to implement the 
accelerator device specic logic on 
the respective host. e accelerator 
devices are used to perform the time 
to frequency domain conversion.
Communication between host and 
accelerator device is performed 
asynchronously in combination 
with multi-buering.
In most cases the initial size chosen 
for a tuple to be optimal for a 
particular type of accelerator device 
usually implies that it contains data 
values for many frequency domain 
operations. Hence this FUNCTOR 
PE performs an integration operation 
to aggregate the auto-correlated 
results for the frequency domain 
operations performed within a tuple.
SPADE Application Dataow Graph
e SOURCE PE is responsible for 
ingesting, parsing and collating 
digitized real value time domain 
antenna data into tuples. e tuple 
size chosen must be optimal for the 
type of accelerators used. 
Implementing compute intensive 
algorithms must be optimized for 
the accelerators unique architecture 
type. Full use of the accelerator 
devices performance primitives and 
internal parallelisim must be 
exploited where possible. Moreover 
careful consideration is required for 
the host to accelerator device 
communication link.
e following SPADE application represented by the dataow graph of its 
respective data-stream connected PEs performs a frequency domain conversion 
followed by an auto-correlation and integration to produce the power spectral 
density of the signal detected by a single antenna. e application makes use of 
computing nodes that have access to accelerators such as Cell BEs, GPUs, 
FPGAs, etc. for performing the frequency domain conversions.
e SPLIT PE distributes tuples to 
the PEs with underlying accelerator 
device enabled hosts. Eectively the 
SPLIT operation is used to provide 
scalability and redundancy.
e resultant tuple from the nal 
integration step are received by this 
UDOP PE which provides a live 
visualisation of the power spectral 
density. 
is FUNCTOR PE receives 
complex number value frequency 
domain data and performs the 
auto-correlation computation. is 
steps reduces the data and produces 
real numbers values.
e  AGGREGATE PE performs a 
summation and averaging for a 
specied integration. At this stage 
the original huge volume of data 
has been signicantly reduced.
Data Flow Motion
SOURCE
UDOP
UDOP
FUNCTOR
FUNCTOR
FUNCTOR
FUNCTOR
SINK
UDOP
SPLIT AGGREGATE
Accelerators References
e SPADE auto-correlation spectrometer application has been successfully tested using Cell BE accelerators in observations made with the AUT 
12m radio telescope at Warkworth, NZ. Current on going research is focussed on increasing the host to accelerator device communication 
bandwidth as well as using NVIDIA GPU based accelerators. InfoSphere Stream’s exibility to accommodate various hardware architectures provides 
a platform for carrying out extensive comparison analysis between various hardware architectures. Comparison of various commodity computing 
architectures will develop a better understanding towards which types of accelerators are best suited for particular stream-data pipeline operations.
[1] Bollard, C., Farrell, D. M, Lee, M., Stone, P. D., ibault, S., & Tucker, S., (2010). IBM InfoSphere Streams: Harnessing Data in Motion.
 IBM Redbooks: International Business Machines Corporation.
[2] IBM Corporation (2009). System Infrastructure for Streaming.
 Retrieved from http://domino.research.ibm.com/comm/research_projects.nsf/pages/esps.Projects.html
[3] Gedik, B., Andrade, H., Wu, K., Yu, P. S., & Doo, M., (2008). SPADE: the system S declarative stream processing engine.
 Proceedings of the ACM SIGMOD international conference on Management of data. DOI: 10.1145/1376616.1376729
InfoSphere Streams & the SKA
e phased approach to SKA construction and Exascale data processing/transport requirements demand computing technology that 
combines vast data processing with high scalability. InfoSphere Streams is a data stream management system (DSMS) middleware 
designed to ingest, lter, analyze and correlate enormous amounts of data incoming from an unlimited number of data stream sources. 
Streams' intention is to enable organizations and institutions to rapidly respond to their respective changing environments without 
the need to store and later process huge amounts of data. Streams aims to full its intention by achieving the following objectives [1]:
 • e ability to scale up or cluster to a wide variety of hardware architectures as the demand for more processing power increases.
 • Provide automation for the handling of data streams that is responsive to changing user requirements as well as data and system
  resource availability.
 • Incremental tasking for changing data schemes and types.
 • Secure transmission of data streams at all system levels, along with comprehensive auditing of the execution environment.
Streams is designed to be highly scalable, so it can be deployed on a single or thousands of computing nodes that may vary with respect 
to their hardware architecture. e runtime environment or Stream Processing Core (SPC) executes numerous long running queries, 
which Streams refers to as jobs [2]. e jobs (or long running queries) are represented by a dataow graph, where each vertex in the 
graph is referred to as a Processing Element (PE) and each connecting edge is in actual fact a data stream. 
Streams Processing Application Declarative Engine (SPADE)
Constructing a distributed stream processing application is a complex process. Essentially the programmer must make the
following considerations:
 • e data stream transform operations which must be developed. Each set of transform operations is a building block in the
  chain of transformations that ingest, process, analyze and produce the desired output data stream.
 • How the data stream transform operations should be deployed to a distributed computing environment to maximize eciency  
  so that results are produced accurately and in a timely manner.
 • e interconnections, network protocols, scheduling and synchronization of operations between the available computing nodes.
SPADE is designed to conceal the aforementioned considerations thus allowing the programmer to focus solely on the design of a 
distributed stream processing application. SPADE fulls its design objectives by collaborating with the SPC to provide a dynamic 
runtime code generation framework capable of achieving scalability and performance through automatic deployment and 
optimization [3]. e SPADE language contains generalized relational stream operators. e stream operators are used to construct 
relational long-running queries. UDOPs (User Dened OPerators) are used for specic situations that the generalized operators 
cannot full. e UDOPs themselves can be written in Java or C++.


An InfoSphere Streams based approach for implementing an FX-style auto-correlation spectrometer with accelerator support

Open Research

http://aut.researchgateway.ac.nz/bitstream/handle/10292/1767/acorrelatorSPADE_1.pdf?sequence=2&isAllowed=y

An InfoSphere Streams based approach for implementing an FX-style auto-correlation spectrometer with accelerator support

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