1. Ramazan Bitirgen, Engin Ipek and Jose F.Martinez MICRO’08 Presented by PAK,EUNJI Coordinated Management of Multiple Interacting Resources in Chip Multiprocessors : A Machine Learning Approach

2.  Resource sharing problem in CMP  Increasing levels of pressure on shared system resources  Efficient sharing is necessary for high utilization and performance  Multiple interacting resources  Cache Space, DRAM Bandwidth and Power Budget  Allocation of a resource affects demands of other resources  Propose a resource allocation framework  At runtime, monitors the execution of each application and learns a predictive model of performance as a function of resource allocation decisions and periodically allocates resources to each core using the model Introduction

3.  Per-application HW performance model  Use Artificial Neural Networks (ANNs)  Predict each app’s performance as a function of the resources allocated to it  Global resource manager  At every interval, searches the possible resource allocations by querying the application performance model Resource Allocation Framework

4.  Use ANNs  Input units, hidden units and an output unit connected via a set of weighted edges  Hidden(output) unit calculates a weighted sum of their inputs(hidden values) based on edge weights  Edge weights are trained with training examples (data sets) How to Predict a Performance? (Artificial Neural Networks)

5.  Input units  L2 cache space, off-chip bandwidth, power budget  Number of read hits, read misses, write hits, and write misses over the last 20K inst and over the 1.5M inst  Fraction of cache ways that are dirty (the amount of WB traffic)  Activation function  Use sigmoid (integer to value in [0, 1])  Model performance as a function of its allocated resources and recent behavior  Training during first 1.2 billion cycle with randomly allocated resource  Always keep a training set consisting of 300 points  Retrained at every 2,500,000 cycle How to Predict a Performance? (Adaptation to per-APP Performance Model)

6.  Optimization  Prevent memorizing outliers in a sample data  Cross validation  Data set is divided into N equal-sized folds (N-1 training sets and 1 test set)  Ensemble consists of N ANN models  Performance is predicted averaging the predictions of all ANNs in the ensemble  Prediction error is estimated as a function of CoV of the predictions by each ANN in the ensemble (will be used for resource allocation) How to Predict a Performance? (Adaptation to per-APP Performance Model) Training Test Trning Test

7.  Make resource allocation decision (at every 500,000 cycle) using the trained per-application performance model  Discard queries involving an app with a high error estimate  Fairly distribute resources to the running applications  Predict the perf and compute the prediction error  If the performance is estimated to be inaccurate (error > 9%), app is excluded from global resource allocation  Search the space with stochastic hill climbing  It starts with a random solution, and iteratively makes small changes to the solution, each time improving it a little.  When the algorithm cannot see any improvement anymore, it terminates  2,000 trials produces the best tradeoff between search performance and overhead Resource Allocation

8.  HW implementation  Single HW ANN and multiplex edge weights on the fly to achieve 16 ‘virtual’ ANNs  12 * 4 + 4 multipliers as many as weighted edges  50 entry-table-based quantized sigmoid function  Calculate in a pipelined manner  Prediction(search) takes 16 cycles for 16 virtual ANNs  Area, Power, and Delay  3% of the chip’s area  3W power consumption  Possible to make 2,000 queries within 5% of interval  OS Interface  Embed training set and the ANN weights to the process state  OS communicates the desired objective function through CR Implementation & Overhead

9.  Tools & architecture  Heavily modified version of SESC  With Wattch(power), HotSpot(temperature)  Baseline : Intel’s Core2Quad, DDR2-800  4-core CMP, frequency = 0.9GHz-4.0GHz(0.1GHz unit)  4MB, 16-way shared L2 cache  Distributed 60W power budget among 4 apps via per-core DVFS  Outs is limited to 57W  Statically allocate 5W  Partition L2 cache space at the granularity of cache ways  Allocate one way to each app  Distribute the remaining 12 ways  Each app statically allocated 800MB/s of off-chip DRAM bandwidth and the remaining 3.2GB/s is distributed Experimental Setup

10.  Metrics  Weighted speedup  Sum of IPCs  Harmonic mean of normalized IPCs  Weighted sum of IPCs  Workload  9 quad-core multi-programmed workloads from SPEC2000 and NAS suites  Classify into 3 categories  CPU-bound  Memory-bound  Cache Sensitive Experimental Setup

11.  Configurations  Unmanaged  Isolated Cache Management (Cache)  Utility-based cache partitioning, MICRO’2006  Distribute L2 cache ways to minimize miss rate  Isolated Power Management (Power)  An analysis of efficient multi-core global power management policies : Maximizing performance for a given power budget, MICRO’2006  Isolated Bandwidth Management (BW)  Fair Queuing Memory System, Micro ‘06  Uncoordinated Cache + Power, Cache + BW, Power + BW, Cache + Power + BW  Continuous Stochastic Hill-Climbing (Coordinated-HC)  Learning based SMT processor resource distribution(issue-queue, ROB, and register file), ISCA ’06  Fair-share  Proposed scheme (Coordinated-ANN)  ANN-based models of the applications’ IPC response to resource allocation are used to guide a stochastic hill-climbing search Experimental Setup

12.  Performance  Results are normalized to Fair-Share  14% average speedup over Fair-Share  Similar for other metrics Evaluation Results P,C,P,MM,C,P,MC,C,C,CP,C,M,CC,M,C,CC,P,C,MC,M,M,CP,C,P,MP,C,P,P

13.  Sensitivity to confidence threshold  Results are normalized to Fair-Share Evaluation Results P,C,P,MM,C,P,MC,C,C,CP,C,M,CC,M,C,CC,P,C,MC,M,M,CP,C,P,MP,C,P,P

14.  Confidence estimated mechanism  Fraction of the total execution time where the ANN could predict the resource allocation optimization for each application Evaluation Results P,C,P,MM,C,P,MC,C,C,CP,C,M,CC,M,C,CC,P,C,MC,M,M,CP,C,P,MP,C,P,P

15.  Proposed a resource allocation framework that Manages multiple shared CMP resources in a coordinated fashion through ANNs and periodic resource allocation scheme  Coordinated approach to multiple resource management is a key to delivering high performance in multi- programmed workloads Conclusions

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