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High Performance Computing On Laptops With Multicores & GPUs. Sushil K. Prasad Computer Science [email protected] About me. Research Area: Parallel and Distributed Algorithms and Systems - over multicores , GPUs, clusters, sensors, handhelds, web services, …

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High performance computing on laptops with multicores gpus

High Performance Computing On Laptops With Multicores & GPUs

Sushil K. Prasad

Computer Science

[email protected]


About me
About me

Research Area: Parallel and Distributed Algorithms and Systems

- over multicores, GPUs, clusters, sensors, handhelds, web services, …

Lab: Distributed and Mobile Systems (DiMoS)

at Ga. Tech campus, 5 PhD students, 2 M.S. students

  • IEEE TCPP Chair (elected)

  • 2 NSF grants – currently looking for PhD/MS/undergraduate students

    • Distributed Algorithms

    • High Performance Cloud Computing


Multicore gpu chips inside a laptop 100s of processors
Multicore & GPU Chips Inside a Laptop - 100s of processors


Gpus vs multicores
GPUs Vs Multicores

  • Combined power exceeds 180 GFLOPs


Intel core 2 duo multicore
Intel Core-2 Duo Multicore

  • Difficult to parallelize

  • Memory hierarchy is a barrier:

    • 1 cycle core

    • 3 cycles L1 cache

    • 14 cycles L2

    • 250 cycles RAM


Gpu graphics processing unit
GPU: Graphics Processing Unit

  • Nvidia 280 GTX

  • 240 cores

  • Extreme memory hierarchy

    • Registers

    • Local memory

    • Shared memory/8 cores

    • Off chip Global Memory

    • bottleneck bus to CPU


  • Nvidia 8800 GTX

  • Smith Waterman Seq Alignment, Fasta, and Blast

  • Database: SwissProt

  • Manavskiand Valle 2008


Parallel data structures priority queues

2

6

7

5

5

6

3

8

8

1

7

9

14

10

9

65

34

38

19

21

12

12

15

16

13

14

23

25

Parallel Data Structures -Priority Queues

  • Large Scale Event Simulation

    • Immune System Simulation

    • VLSI Logic simulation

  • Branch and Bound

  • Task Scheduling

  • Challenge: Fine Grained Systems

  • Students: DineshAgarwal, Nick Mancuso



Legacy code to gpus student chad christopher
Legacy-Code to GPUs(Student: Chad Christopher)



Np hard distributed problems in networks
NP-Hard Distributed Problems in Networks Networks

NSF Grant

  • Minimum Vertex/Target Cover

  • Minimum Triangle Packing

  • Optimum mobile sensor network target tracking

  • Minimum channel assignment in mobile ad-hoc networks

  • Students: John Daigle, ThamerSulaiman


Middleware for mobile ad hoc applications

2. Lookup Networks

1. Register

Middleware for Mobile Ad–hoc Applications

Mobile Support Station

Applications

Deviceware

Process Requests

3. p2p communication

Listener

Applications

Applications

Deviceware

Process Requests

Listener

Deviceware

Groupware

Process Requests

Listener

Listener

Process Requests

UM-Morris

Directory


Bondflow distributed workflow over web services student janaka balasooriya
BondFlow Networks : Distributed Workflow over Web Services(Student: JanakaBalasooriya)

  • Web service interface module

  • Proxy object generator module

  • Workflow configuration module

  • Execution module.

  • Mobile Web Services

Web Service Interface Module

Lookup for Web services

Web Services Registry (UDDI)

S

O

A

P

WS Locator

WSDL

WSDL Parser

Parsed WSDL

Workflow Execution Module

Proxy Object Generator Module

Web Bond Runtime

SOAP/ SyD

Workflow Configuration Module

JVM


P2p search based on bayesian decision and value of information voi student rasanjalee

A Priori Uncertainty Networks

: U1

A Posterior Uncertainty

: U2

U1 –U2 = Information

P2P Search based on Bayesian Decision and Value of Information (VOI) – (Student: Rasanjalee)

The meaning of Uncertainty based Information

  • Peer Selection:

    • Sending/forwarding query at each node along query path

      = series of decision making steps based on incomplete data

    • A decision step: query the node that will reduce the uncertainty of current belief most.

  • Experimental Results:

The reduction in uncertainty at each decision step

Current Belief

Decision step 1

.

.

.

.

Decision step n


Middleware on distributed smart cameras
Middleware on Distributed Smart Cameras Networks

  • Middleware on DSC networks

    • provide a high-level programming interface for applications.

    • simplify the development of distributed applications on DSC networks.

    • provide networking functionality as part of the middleware

  • Student: JayampathiSampat

cmucam3


About me1
About me Networks

Research Area: Parallel and Distributed Algorithms and Systems

- over multicores, GPUs, clusters, sensors, handhelds, web services, …

Lab: Distributed and Mobile Systems (DiMoS)

at Ga. Tech campus, 5 PhD students, 2 M.S. students

  • IEEE TCPP Chair (elected)

  • 2 NSF grants – currently looking for PhD/MS/undergraduate students

    • Distributed Algorithms

    • High Performance Cloud Computing


High performance computing on laptops with multicores gpus1

High Performance Computing On Laptops With Networks Multicores & GPUs

Sushil K. Prasad

Computer Science

[email protected]


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