We estimate an application’s virtual execution time as the duration for which the application should have run using all the resources on the chip in order to execute the same number of instructions as it did in the actual execution.

Tapestry: Reducing Interference on Manycore Processors for IaaS Clouds

Dynamically Partitioned Manycore Architecture

Online PerformanceEstimation (pTables)

Higher-OrderDecisions

1. Dynamically Partitioned Manycore Architecture > CacheBlocs – Scalable Non-associative Cache Partitioning > Prefetcher Throttling – Efficient Bandwidth Utilization2. Online Performance Estimation (pTables) > Flattened Partial LRU Vector (FPLV) – Shadow Cache Statistics > Shadow Prefetcher – Shadow Prefetcher Statistics3. Higher-Order Decisions > Virtual Time Metering (VTM) – Accurately charge customers > Simultaneous Performance Optimization Table (SPOT) –

Maximize throughput while maintaining fairness

Novel Microarchitectural Components in Tapestry

Basic Problem: Excessive resource interference hinders adoption of Manycores in IaaS Clouds

Our Approach: Tapestry

Anshuman Gupta and Michael Bedford Taylor CSE Department, University of California at San Diego

I. While existing techniques can overcharge consumers by as much as 12x, Tapestry does fair metering.

Overview

Results

The Details

III. Tapestry improves overall-throughput by as much as 1.8x.

IV. With increasing application load, Tapestry provides progressively better overall-throughput as well as worstcase-performance. Area and Energy Costs in Tapestry

Manycore Architectures have many potential benefits forInfrastructure-as-a-Service (IaaS) Clouds

Virtual Time Metering (VTM)

Simultaneous Performance Optimization Table (SPOT)

Fair Slowdown Metric, an approximate geometric mean of application virtual times, when maximized increases throughput while maintaining fairness.

Performance Tables (pTables) store the performance estimates for all applications for a spectrum of allocations of the last level cache and memory bandwidth.

Flattened Partial LRU Vector (FPLV)

To determine the shadow prefetcher statistics, we run the prefetching algo without actually prefetching data.

Shadow Prefetcher

Shadow caching for DSP requires tracking the LRU orders for different cache sizes. We efficiently maintain all these LRU orders using a topologically sorted vector.

CacheBlocsOur cache uses Dynamic Set Partitioning (DSP) and Tag-Indirect Cache Addressing (TICA) for scalable cache partitioning.

Prefetcher ThrottlingWith dynamic bandwidth allocation, we change prefetcher aggression to maximize bandwidth utilization.

Tapestry is a distributed manycore architecture with dynamically partitioned last-level cache and memory bandwidth, shared between many applications.

II. Slowdowns are imminent, but Tapestry improves worstcase-performance by as much as 1.6x.

Power – High performance per wattSpace – High performance per rack

Hardware – Low cost per processing core

I. How much to charge the consumers, also called metering?II. How to minimize the slowdowns of concurrent applications?III. How to maximize the throughput of co-located applications?IV. Can we handle higher application load on a single processor?

1. Dynamically partition the critical shared resources.2. Estimate application performances for all resource allocations.3. Use the performance estimates to make higher order decisions.

To calculate pTables, we use an online analytical performance model that uses cache and prefetcher statistics for all configurations.

We use a dynamic algorithm in hardware to find the resource distribution that will maximize the Fair Slowdown Metric.

We charge the consumer for using the entire chip for this estimated virtual time.

Dynamic resource sharing leads to unpredictable slowdowns for applications. Interference gets worse with increasing number of concurrent applications.

Interference leads to Higher-Order Problems for IaaS Clouds

• We were able to estimate the pTables with an error of just about 1%.

• Using CacheBlocs we were able to reduce power consumption in partitioned caches by 67% .

• We were able to approximately track the pareto optimal curve for prefetcher performance with our throttling.

Additional Results