Rodrigo Fernandes de Mello, Evgueni Dodonov, José Augusto Andrade Filho

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1 Middleware for High Performance Computing Rodrigo Fernandes de Mello, Evgueni Dodonov, José Augusto Andrade Filho University of São Paulo São Carlos, Brazil {mello, eugeni,

2 Outline Goal How it works MidHPC example Historical information

3 Main Goal Started in 2003 Support the execution of legacy multithreaded applications on distributed environments Clusters Grids Users and developers do not have to worry about the environment characteristics and load when executing or writing applications Developers just have to modularize application in threads

4 How? Linux and future support for Unixes Intercepting the creation of threads Create processes (totally transparent to users) But how processes will communicate? Distributed Shared Memory DSM How DSM works? It is a distributed layer on any network filesystem A file in such FS represents the shared memory Processes map such file in main memory Any memory modification is updated in FS

5 How? How DSM works? The filesystem can provide distribution, security etc We also have another DSM version (/dev) Process execution traces are stored in DSM We can know historical behaviour Information used to understand future behaviour Aiming Load balancing and considering: Migration cost (cost to transfer processes to idler processors) Network usage and Process communication Hard disk usage Main and Swap Memory usage

6 How? How do we obtain process information on the fly? First Version Instrumentation of Linux kernel Obtain process cpu, network, memory and hard disk usage Requires kernel modification (a patch easily applied and recompilation) Experiments confirm less than 1% of overhead on Linux (gathering information at constant periods of 100 ms)

7 How? How do we obtain process information on the fly? Second Version Process monitor The monitor is launched transparently when the user starts the application (using the same concept for creating processes) The monitor gathers information based on events such as network messages, hard disk reads and writes, and calculates the processing cost and memory accesses

8 How? How do we obtain process information on the fly? After getting information Classification using an ART 2A self organizing neural network architecture, defining process behaviours or states Such states represents load situations (processing, network etc) The states and their transitions are represented by a Markov chain

9 How? Having the information The Markov chain can also be seen as a Time series The time series allows defining historical behaviour When the same application is launched again we can better schedule it, by knowing its behaviour The time series is used for predictions By knowing the current behaviour we may predict the future Using neural networks, ARIMA (statistics) and other statistical methods

10 How? Having the information By knowing historical behaviour and understanding future Load balancing optimizations The objective is to reduce the application execution time Optimization function (OF) considers: Process communications Process hard disk accesses Process memory accesses Process CPU usage The OF also considers environment capacities: CPU (Mips, Mflops), network links (latency), hard disk (throughput), main and swap memory latency

11 How? Having the information By knowing historical behaviour and understanding future We can also: make Data Prefetching as we have a good idea about when they are useful define a neighborhood to distribute processes according to their communication make Automatic Process Transfers (migrations) as we know where they better run (idler processors) Considering all environment and process characteristics

12 Application

13

14

15 Intercepting Layer

16 Intercepting Layer

17 Intercepting Layer

18 Intercepting Layer

19 Intercepting Layer Load Balancer (firstly history - neighborhood)

20 Load Balancer (firstly history - neighborhood)

21 Meanwhile: Process and environment information are stored in the DSM Migrations occur when computers are overloaded (using historical data and predictions) Data prefetching according to predictions

22 Applications DSM Intercepting Layer Load Balancer Extracting Behavior and Classification Operating System

23 Applications DSM Intercepting Layer Load Balancer Extracting Behavior and Classification READY Linux 2.4 and 2.6 and also the Monitor Operating System

24 Applications DSM Intercepting Layer Load Balancer First version for July 2007 Extracting Behavior and Classification Operating System

25 Applications DSM Intercepting Layer Ready Load Balancer Extracting Behavior and Classification Operating System

26 Applications DSM Two versions Intercepting Layer Load Balancer Extracting Behavior and Classification Operating System

27 P2P load balancing Project Historical Information Model for resource load evaluation Load balancing algorithm for Grid Performance Evaluation of HPC Libraries (MPI, PVM, Gamma, TCP/IP etc) Process migration model using lifetime workload prediction Scheduling policy for Grids considering replication 2002 Scheduling policy considering communication prediction and network latency 2003 High Availability Support for Linux 2004 Scheduling decisions based on parallel application knowledge Network Evaluation of LAN, MANs and WANs 2005 Process Scheduling using Ant Colony Optimization Modeling Heterogeneous HPC Environments

28 Project Historical Information Data prefetching using knowledge extracted from parallel applications (Neural Networks and Stochastic Tecniques) The Route Load Balancing for Grid Environments RouteGA: A new Grid Load Balancing Technique Optimizing Distributed Data Access on Grids using Neural Networks and Genetic Algorithms A New Memory Slowdown Model to Characterize HPC applications 2006 A Model for Extraction, Classification and Prediction of HPC Application behaviour Instance-based Learning to predict HPC Application Behaviour 2007 Proposal of a new Neural Network to detect novelties applied to detect HPC fault events Current Work: New Load Balancing optimization techniques for Grid Environments

29 Middleware for High Performance Computing Rodrigo Fernandes de Mello, Evgueni Dodonov, José Augusto Andrade Filho University of São Paulo São Carlos, Brazil {mello, eugeni,

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