Presentation is loading. Please wait.

Presentation is loading. Please wait.

Approved for public release, distribution unlimited Preliminary Study toward Intelligent Run-time Resource Management Techniques for Large Tiled Multi-Core.

Published byCecily Kelley Alexander Modified over 9 years ago

Similar presentations

Presentation on theme: "Approved for public release, distribution unlimited Preliminary Study toward Intelligent Run-time Resource Management Techniques for Large Tiled Multi-Core."— Presentation transcript:

1 Approved for public release, distribution unlimited Preliminary Study toward Intelligent Run-time Resource Management Techniques for Large Tiled Multi-Core Architectures Dong-In Kang, Jinwoo Suh, Janice O. McMahon, and Stephen P. Crago University of Southern California Information Sciences Institute September 18, 2007 This effort was sponsored by Defense Advanced Research Projects Agency (DARPA) through the Dept. of Interior National Business Center (NBC), under grant number NBCH1050022. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsement, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), Dept. of Interior, NBC, or the U.S. Government.

2 Approved for public release, distribution unlimited 2 Outline Introduction Simulation Model Applications and Results  Resource Fragmentation  Mapping Performance Problem Space and Future Work Conclusion

3 Approved for public release, distribution unlimited 3 Motivation Rising processor density, multi-core architecture  MIT Raw 4x4  8-core 64-thread SUN Niagara 2 processor  Tilera TILE64 processor  Intel 80-core Teraflops prototype processor  In the future: 32x32 or larger Dynamic applications  With large-scale architectures, multiple applications will share tile array; need to arbitrate resources between applications  Requirements are unpredictable and changing in response to environment  Need for dynamic mapping, allocation/deallocation, resource management Need for more intelligent run-time systems  Better resource allocation for efficiency  New system capabilities to alleviate programming burden  Dynamic reactivity for changing scenarios  Complex optimization space

4 Approved for public release, distribution unlimited 4 Research Goal: Intelligent Run-Time Resource Allocation Application of knowledge-based and learning techniques to the problem of dynamic resource allocation in a tiled architecture  Planning and optimization of resource usage given application constraints  Knowledge derived from dynamic performance profiling, modeling, and prediction  Run-time learning of resource allocation trade-offs Proof-of-concept demonstration: simulation of application workload to compare performance of manual and automatic resource allocations  Define representative application  Develop scalable simulation  Define resource mapping problem and apply intelligent technique Current work: definition of problem, construction of simulator, and preliminary study to quantify performance effects

5 Approved for public release, distribution unlimited 5 System Framework Machine-level Resource Allocation Application-level Task Composition Knowledge- base Application Environment Generate tasks that meet application goals in current environment Task graph, performance requirements Performance monitoring/ knowledge buildup Processing Environment Task assignment to maximize machine performance Profiling Prototype experiments in machine-level resource allocation Simulation environment Research focus

6 Approved for public release, distribution unlimited 6 Outline Introduction Simulation Model Applications and Results  Resource Fragmentation  Mapping Performance Problem Space and Future Work Conclusion

7 Approved for public release, distribution unlimited 7 ISI Simulator for Multi-Core Architecture Event-driven simulator  In SystemC  Based on ARMn multiprocessor simulator from Princeton Univ. Generic processor module  Proportional share resource scheduler  Memory latency is implicit in computation time  Parameterized specification: CPU speed, CPU-network interface speed, etc. Network  One network, 2-D mesh  5x5 crossbar switch  Message passing model  Store and forward routing  Parameterized specification: speed, buffer size, packet overhead, etc. PE Switch Tile Profile info: Per link: Data amount Blocking time Per packet: Latency Blocking time Per task: Event log Blocking time Per CPU: Utilization

8 Approved for public release, distribution unlimited 8 Simulation Approach A B C F E G D H I Application Task Graph Compute Send Recv AABB CDDE FFFG GHII Tile Allocation: 2D Mesh Simulation uses data flow information and network characteristics (topology, latency, overhead, etc.) to produce performance results for a particular task mapping. Each task has a script that defines computation steps, message sends and receives

9 Approved for public release, distribution unlimited 9 Dynamic Behavior t1t1 t2t2 t3t3 t4t4 App AApp BApp CApp Dfree Optimized Mapping t5t5 Time Two performance effects over time: Resource fragmentation: incremental resource allocation decreases locality Dynamic application requirements and resource availability Two performance effects over time: Resource fragmentation: incremental resource allocation decreases locality Dynamic application requirements and resource availability

10 Approved for public release, distribution unlimited 10 Outline Introduction Simulation Model Applications and Results  Resource Fragmentation  Mapping Performance Problem Space and Future Work Conclusion

11 Approved for public release, distribution unlimited 11 Example Application: Radar Resource Management using IRT Drive IRT spreadsheets and morphing scenarios with parameters from a notional radar resource manager GMTI Task Graph input to simulator FAT requirements modeled with statistically varying processor loads, message traffic Subband Analysis Time Delay & Equalization 146MFLOPs Time Delay & Equalization P = C = T = (N srg xN ch xN pri ) Adaptive Beamforming 42MFLOPs Adaptive Beamforming P = C = T = (N ch xN srg xN pri ) Pulse Compression 112MFLOPs Pulse Compression P = C = T = (N bm xN srg xN pri ) Doppler Filtering 32.4MFLOPs Doppler Filtering P = C = T = (N bm xN srg xN pri ) STAP 47MFLOPs STAP Subband Synthesis Subband Synthesis Data Combination Target Detection 4.7MFLOPs 3D Grouping Parameter Estimation 189MFLOPs P = C = T = (N dop x (N bm *N stage )xN srg ) P = N cnb x N srg x N dop C = T = (N cnb x N srg x N dop ) P = C = T = (N cnb x N srg x N dop ) P = C = T = (N cnb x N rg x N dop ) Data Independent N sub

12 Approved for public release, distribution unlimited 12 Resource Fragmentation ******************************** *************............................................................................ ********* *.** *** *****.***** *************** *** ***.** ***** ***.* * ***** * * ******** * **.***.** ***.***.**.******.* ***** ** **** **** ** *** * *** * *** ** * * * * * ********.****** *****. ** ***** **.** ** *** *.*** * ********.* **** **********. ***** * ***.* * ****** ******.*** **. * *.**.** **** ** * **** ***** ******** * *********** *** * *** ***** *** *** ** **** * ** * ******* ****** **** ** ****** **** **** ****** * * * * ** * * * ** ***......................................................................................................................................................................................................................................................................................................*.***....*.***.*...*..*.*..*...*...*.*.. * * **.* * * ** ***** *** * ** ** * ***. *. ** **. * * ****.* * * *** * *** * **** ** ** ** ** *** * ** * ** * ***. ** ** * *.*** **** * ***.* **. * * ***.*** ** * ***. * * **.* * * *******.** **. ***.***** * * * ** ***** * ** * *** ** * ** *** ** * **** * * * * * * * ** * * * ** * **** *** * * * * *** ** *** * * ** **** * *........**.*.........*.............. *.. **.............*.*.............................................................................................................................................................................................................................*...**....**.*.*.*.*.***...*.*.......*.*.**..****.***....**..** ****.*..***.****.****......*..*. **....*.*...*.*.*..*.****.**.**. **..**...**.***.****.*.*.*******..**....**..**..***..***.*..*.** ***...*.*.*.***..*.*.**..***.**. *.*.....*.***....*.*.*....**..**.*****..*.***....*.***.*....*..*...**.*.....***.*****.**.*.....*..****.***...*.*.*.*.*.******.*. *...*..****...*..*.....**..*.... **...*...*..**..*..*..**.**..... ****....**....**..***.**..*.**** ***.*......*.*.**.*.***.*..***.......**.*....*.*.........**.**.*.*.****...**..*.*.*.... * * *..*.*..... * *..*. * *..* *.*** *.*.****.... ***..* ** *..*** **..******.*.. * *.. *. ****..*...**. ** *. *. *. ******.*.*...*.* * ** * *....*...*..*. *.*... **. **. *..*.*...** *.*. *. * *. *..*. *. *..*..*.* *....*.*. *........**...*..... * * *.*.*. **. * * *......*..*.......*.. *.**. * *. *. ***..........*. **.... *......*....*...*.*...*..*...*.* *. **. *.** * ** * ** ** * * ****** * 0: GMTI[525], FAT[460], FREE[39] * GMTI asks to have [525] tiles Allocate GMTI [525] more nodes * FAT asks to have [460] tiles Allocate FAT [460] more nodes 8: GMTI[367], FAT[361], FREE[296] * GMTI asks to have [367] tiles Free GMTI [123] nodes 16: GMTI[251], FAT[355], FREE[418]' * FAT asks to have [355] tiles Allocate FAT [7] more tiles 25: GMTI[419], FAT[446], FREE[159] * GMTI asks to have [419] tiles Free GMTI [400] tiles * FAT asks to have [446] tiles Allocate FAT [241] more tiles Assumptions: - GMTI: mode change - FAT: constrained in computation requirement changes in ±10% - Linear tile assignment - GMTI, FAT can change independently - GMTI has higher priority than FAT Fragmentation happens quickly! ‘*’: GMTI, ‘.’: FAT, ‘ ‘: FREE

13 Approved for public release, distribution unlimited 13 Outline Introduction Simulation Model Applications and Results  Resource Fragmentation  Mapping Performance Problem Space and Future Work Conclusion

14 Approved for public release, distribution unlimited 14 Example: GMTI Application Mapping Task Graph (GMTI) Subband Analysis Time Delay & Equalization 146MFLOPs Adaptive Beamforming 42MFLOPs Pulse Compression 112MFLOPs Doppler Filtering 32.4MFLOPs STAP 47MFLOPs Subband Synthesis Data Combination Time Delay & Equalization 146MFLOPs Adaptive Beamforming 42MFLOPs Pulse Compression 112MFLOPs Doppler Filtering 32.4MFLOPs STAP 47MFLOPs Subband Synthesis Subband Analysis (a) Clustered(b) Fragmented Interference from other application is modeled by random traffic generator ParameterValues GMTI resource requirements 128 tiles868 tiles Free tilesFragmentedClustered Mapping techniqueRandomHeuristic Task frequency1 KHz2 KHz * Total 16 cases

15 Approved for public release, distribution unlimited 15 128-Tile 1 KHz GMTI Performance For high network loads, mappings on fragmented resources cause network load imbalance and longer latencies. In some cases, traffic causes network overload.

16 Approved for public release, distribution unlimited 16 128-Tile 2 KHz GMTI Performance With higher task frequencies (performance), latency is sensitive to both resource allocation method and fragmentation of the resources.

17 Approved for public release, distribution unlimited 17 868-Tile 1 KHz GMTI Performance With higher GMTI allocation, latency is: - less sensitive to background network load - more sensitive to task mapping algorithm than fragmentation of the resources With higher GMTI allocation, latency is: - less sensitive to background network load - more sensitive to task mapping algorithm than fragmentation of the resources

18 Approved for public release, distribution unlimited 18 868-Tile 2 KHz GMTI Performance With higher GMTI allocation and task frequencies (performance), random mapping does not work at all.

19 Approved for public release, distribution unlimited 19 Outline Introduction Simulation Model Applications and Results  Resource Fragmentation  Mapping Performance Problem Space and Future Work Conclusion

20 Approved for public release, distribution unlimited 20 Network-Oriented Problem Space Clustered free tiles / Communication patterns are known - Near optimal mapping is possible - Adaptability to run-time dynamic behavior Clustered free tiles / communication patterns are NOT known - Initially random mapping (possibly quite bad performance) - Learning the communication pattern - Remapping for performance improvement Fragmented free tiles / communication patterns are known - Initially reasonable mapping (still possibly bad performance) - Task migration for enhanced performance and defragmentation Fragmented free tiles / communication patterns are NOT known ahead - Initially random mapping (worst performance) - Learning the communication pattern - Task migration for enhanced performance and defragmentation Task Migration/ Defragmentation Learn communication patterns Learn communication patterns Task Migration/ Defragmentation More intelligence leads to better resource management

21 Approved for public release, distribution unlimited 21 Future Work Run-time profiling technique  Low overhead  Detecting network load imbalance  Detecting communication patterns, dependencies  Detecting processor load imbalance Gradual morphing technique  Intelligent partial remapping of the application Hot spot removal, load balancing  Adaptive changes of parallelism of the application Adaptive to the status of free resources Run-time support for gradual morphing  Task migration  Dynamic changes of parallelism of the application Dynamic adaptation of code  Expression of dynamic changes of code at run-time  Dynamic code generation

22 Approved for public release, distribution unlimited 22 Conclusion Large multi-tiled architectures will require intelligent run-time systems for resource allocation and dynamic mapping  Future applications will be complex and will need to respond to unexpected events  Future, large-scale multi-tiled architectures will require application sharing and arbitration of resources In this research, we have explored the effect of dynamic application variations on multi-tiled performance  Dynamic IRT with representative mode changes and load variations causes resource fragmentation over time  Application mappings must be done carefully in order to provide robust behavior Future work will involve the application of knowledge- based and machine learning algorithms to the profile- based dynamic resource management

Download ppt "Approved for public release, distribution unlimited Preliminary Study toward Intelligent Run-time Resource Management Techniques for Large Tiled Multi-Core."

Similar presentations

Network II.5 simulator ..

Network II.5 simulator ..
Simulation of Feedback Scheduling Dan Henriksson, Anton Cervin and Karl-Erik Årzén Department of Automatic Control.

Simulation of Feedback Scheduling Dan Henriksson, Anton Cervin and Karl-Erik Årzén Department of Automatic Control.
Hadi Goudarzi and Massoud Pedram

Hadi Goudarzi and Massoud Pedram
Resource Management §A resource can be a logical, such as a shared file, or physical, such as a CPU (a node of the distributed system). One of the functions.

Resource Management §A resource can be a logical, such as a shared file, or physical, such as a CPU (a node of the distributed system). One of the functions.
REAL-TIME COMMUNICATION ANALYSIS FOR NOCS WITH WORMHOLE SWITCHING Presented by Sina Gholamian, 1 09/11/2011.

REAL-TIME COMMUNICATION ANALYSIS FOR NOCS WITH WORMHOLE SWITCHING Presented by Sina Gholamian, 1 09/11/2011.
1 Advancing Supercomputer Performance Through Interconnection Topology Synthesis Yi Zhu, Michael Taylor, Scott B. Baden and Chung-Kuan Cheng Department.

1 Advancing Supercomputer Performance Through Interconnection Topology Synthesis Yi Zhu, Michael Taylor, Scott B. Baden and Chung-Kuan Cheng Department.
The Who, What, Why and How of High Performance Computing Applications in the Cloud Soheila Abrishami 1.

The Who, What, Why and How of High Performance Computing Applications in the Cloud Soheila Abrishami 1.
A system Performance Model Instructor: Dr. Yanqing Zhang Presented by: Rajapaksage Jayampthi S.

A system Performance Model Instructor: Dr. Yanqing Zhang Presented by: Rajapaksage Jayampthi S.
Development of Parallel Simulator for Wireless WCDMA Network Hong Zhang Communication lab of HUT.

Development of Parallel Simulator for Wireless WCDMA Network Hong Zhang Communication lab of HUT.
Computer Architecture Introduction to MIMD architectures Ola Flygt Växjö University +46 470 70 86.

Computer Architecture Introduction to MIMD architectures Ola Flygt Växjö University
SKELETON BASED PERFORMANCE PREDICTION ON SHARED NETWORKS Sukhdeep Sodhi Microsoft Corp Jaspal Subhlok University of Houston.

SKELETON BASED PERFORMANCE PREDICTION ON SHARED NETWORKS Sukhdeep Sodhi Microsoft Corp Jaspal Subhlok University of Houston.
Towards Feasibility Region Calculus: An End-to-end Schedulability Analysis of Real- Time Multistage Execution William Hawkins and Tarek Abdelzaher Presented.

Towards Feasibility Region Calculus: An End-to-end Schedulability Analysis of Real- Time Multistage Execution William Hawkins and Tarek Abdelzaher Presented.
A Parallel Computational Model for Heterogeneous Clusters Jose Luis Bosque, Luis Pastor, IEEE TRASACTION ON PARALLEL AND DISTRIBUTED SYSTEM, VOL. 17, NO.

A Parallel Computational Model for Heterogeneous Clusters Jose Luis Bosque, Luis Pastor, IEEE TRASACTION ON PARALLEL AND DISTRIBUTED SYSTEM, VOL. 17, NO.
GridFlow: Workflow Management for Grid Computing Kavita Shinde.

GridFlow: Workflow Management for Grid Computing Kavita Shinde.
Packet-Switched vs. Time-Multiplexed FPGA Overlay Networks Kapre et. al RC Reading Group – 3/29/2006 Presenter: Ilya Tabakh.

Packet-Switched vs. Time-Multiplexed FPGA Overlay Networks Kapre et. al RC Reading Group – 3/29/2006 Presenter: Ilya Tabakh.
NoC Modeling Networks-on-Chips seminar May, 2008 Anton Lavro.

NoC Modeling Networks-on-Chips seminar May, 2008 Anton Lavro.
11/14/05ELEC6200-001 Fall 20051 Multi-processor SoCs Yijing Chen.

11/14/05ELEC Fall Multi-processor SoCs Yijing Chen.
May 14, 20081 Organization Design and Dynamic Resources Huzaifa Zafar Computer Science Department University of Massachusetts, Amherst.

May 14, Organization Design and Dynamic Resources Huzaifa Zafar Computer Science Department University of Massachusetts, Amherst.
An Introduction to Parallel Computing Dr. David Cronk Innovative Computing Lab University of Tennessee Distribution A: Approved for public release; distribution.

An Introduction to Parallel Computing Dr. David Cronk Innovative Computing Lab University of Tennessee Distribution A: Approved for public release; distribution.
1 Introduction to Load Balancing: l Definition of Distributed systems. Collection of independent loosely coupled computing resources. l Load Balancing.

1 Introduction to Load Balancing: l Definition of Distributed systems. Collection of independent loosely coupled computing resources. l Load Balancing.

Similar presentations

Ads by Google