meta-balancer: automating load balancing decisions

Meta-Balancer: Automating Load Balancing Decisions HPC applications are increasingly becoming complex and dynamic. Many applications require dynamic load balancing to achieve high performance and system utilization. Different applications have different characteristics and hence need to use different load balancing strategies. There are many load balancing algorithms available. However, invocation of an unsuited load balancing strategy can lead to inefficient execution. Most commonly, the application programmer decides which load balancer to use based on some educated guess. We propose Meta- Balancer, a framework to automatically decide the best suited load balancing strategy. Meta-Balancer monitors application characteristics and based on that, it chooses an ideal load balancing algorithm to use. In order to predict the best load balancing strategy, Meta-Balancer uses a supervised random forest machine learning technique with the application characteristics as the features. Using this, we are able to achieve high prediction accuracy of 82% on the test set to demonstrate performance benefits of up to 3X. Harshitha Menon, Kavitha Chandrasekar, Laxmikant V. Kale Motivation •Load imbalance is a critical factor that can affect the performance of an application •Different applications require different load balancing strategies •Application programmer has to choose from numerous load balancing algorithms •Puts a burden on the user and may not be most efficient Meta-Balancer Summary • Monitors system and application characteristics • Statistics collected from the application is used to predict the most suitable load balancer • Decision tree can be used to guide the selection of statistics • Proposed Meta-Balancer to automate the load balancing decision • Meta-Balancer strategy selection collects statistics of the application and machine to choose the best suitable load balancer • Meta-Balancer uses random forest machine learning technique to choose the load balancing strategy • Meta-Balancer is able to predict the best suitable load balancer with 82% accuracy on the test set University of Illinois at Urbana-Champaign • Supervised Random Forest machine learning technique is used to predict load balancing strategy • Choices are : GreedyLB, RefineLB, MetisLB, HybridLB, DistributedLB, ScotchLB, NoLB • Application & machine characteristics used as features • Synthetic Benchmark used to generate training set • Accuracy on test set is 82% • Trained model is used to predict on other mini-apps Prediction accuracy Communication Intensive configuration Computation Intensive configuration Decision tree •Used to study denotation shock dynamics •Suffers from load imbalance •Meta-Balancer is able to choose a good load balancer •Meta-Balancer improves performance Applications score = T predicted_lb / T best_lb -1 Larger the score, worse the performance Framework to automate the load balancing decision Lassen – LLNL Proxy App Particle-In-Cell Simulation •Used for simulation of plasma particles •Load imbalance results from imbalanced distribution of particles and particle motion •Meta-Balancer is able to improve performance with weak-scaling Predictions 0 10 20 30 40 50 60 70 80 90 128 256 512 1024 App time (s) Number of cores No LB Worst LB Best LB Predicted LB 0 10 20 30 40 50 60 70 80 128 256 512 1024 2048 App time (s) Number of cores No LB Worst LB Best LB Predicted LB high avg utilization No LB N start high comm load / compute load Y Y N high rate of change Y DistLB/Hierarchical LB large migration cost N high benefit vs overhead of central Y N N Y GreedyLB ReﬁneLB topology + hop info N Comm Refine LB Comm LB high rate of change Y N high rate of change Y N Y Topo LB Topo Reﬁne LB Prediction scores

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Meta-Balancer: Automating Load Balancing Decisions

HPC applications are increasingly becoming complex and dynamic. Many applications require dynamic load balancing to achieve high performance and system utilization. Differentapplications have different characteristics and hence need to use different load balancing strategies. There are many load balancing algorithms available. However, invocation of an unsuitedload balancing strategy can lead to inefficient execution. Most commonly, the application programmer decides which load balancer to use based on some educated guess. We propose Meta-Balancer, a framework to automatically decide the best suited load balancing strategy. Meta-Balancer monitors application characteristics and based on that, it chooses an ideal loadbalancing algorithm to use. In order to predict the best load balancing strategy, Meta-Balancer uses a supervised random forest machine learning technique with the applicationcharacteristics as the features. Using this, we are able to achieve high prediction accuracy of 82% on the test set to demonstrate performance benefits of up to 3X.

Harshitha Menon, Kavitha Chandrasekar, Laxmikant V. Kale

Motivation

•Load imbalance is a critical factor that can affect the performance of an application•Different applications require different load balancing strategies •Application programmer has to choose from numerous load balancing algorithms•Puts a burden on the user and may not be most efficient

Meta-Balancer

Summary

• Monitors system and application characteristics

• Statistics collected from the application is used to predict the most suitable load balancer

• Decision tree can be used to guide the selection of statistics

• Proposed Meta-Balancer to automate the load balancing decision

• Meta-Balancer strategy selection collects statistics of the application and machine to choose the best suitable load balancer

• Meta-Balancer uses random forest machine learning technique to choose the load balancing strategy

• Meta-Balancer is able to predict the best suitable load balancer with 82% accuracy on the test set

University of Illinois at Urbana-Champaign

• Supervised Random Forest machine learning technique is used to predict load balancing strategy• Choices are : GreedyLB, RefineLB, MetisLB,

HybridLB, DistributedLB, ScotchLB, NoLB• Application & machine characteristics used as

features• Synthetic Benchmark used to generate training set• Accuracy on test set is 82%• Trained model is used to predict on other mini-apps

Prediction accuracy

Communication Intensive configuration Computation Intensive configuration

Decision tree

•Used to study denotation shock dynamics•Suffers from load imbalance•Meta-Balancer is able to choose a good load balancer•Meta-Balancer improves performance

Applications

score = Tpredicted_lb / Tbest_lb-1Larger the score, worse the performance

Framework to automate the load balancing decision

Lassen – LLNL Proxy App

Particle-In-Cell Simulation•Used for simulation of plasma particles•Load imbalance results from imbalanced distribution of particles and particle motion•Meta-Balancer is able to improve performance with weak-scaling

Predictions

128 256 512 1024

App

tim

e (s

)

Number of cores

No LBWorst LB

Best LBPredicted LB

128 256 512 1024 2048

App

tim