A Framework for Providing Quality of Service in Chip Multi-Processors
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1 A Framework for Providing Quality of Service in Chip Multi-Processors Fei Guo 1, Yan Solihin 1, Li Zhao 2, Ravishankar Iyer 2 1 North Carolina State University 2 Intel Corporation The 40th Annual IEEE/ACM International Symposium on Microarchitecture 1
2 Background Chip Multi-Processor (CMP) is mainstream architecture Some platform resources (cache, bandwidth) are shared Resource sharing leads to contention Contention may result in a large performance variation Future uses of CMP Run diverse applications with diverse requirements Require performance Quality of Service (QoS) 2
3 Related Work Previous QoS frameworks [Iyer04][Yet05] [Hsu06] [Rafique06] [Nebit07][Iyer07] QoS target specified as IPC or miss rate Resource partitioning (cache, off-chip bandwidth) Resource manager Allocate resource to reach all applications QoS targets Previous QoS frameworks do not fully provide QoS 3
4 Problems with Previous Frameworks 4-core CMP IPC Target IPC Number of concurrent applications (bzip2) QoS targets not met when > 2 jobs run simultaneously CMP cannot check if available resources are sufficient CMP does not know when to reject jobs 4
5 Contributions A framework to provide QoS in a CMP Appropriate QoS target Allowing admission control policy QoS execution modes Important for flexibility and throughput Safe throughput optimization techniques Preserving QoS QoS execution mode downgrade Resource stealing 5
6 Outline QoS target specification QoS execution modes Resource stealing Evaluation Conclusions 6
7 QoS Targets for Individual Jobs Performance Metrics IPC or cache miss rate Resource Usage Metrics (RUM) Cache size, bandwidth rate Easily comparable Foundation for constructing admission control Cannot be ill-defined More familiar to the users 7
8 Timeslot Resource t maximum wall-clock time Borrowed from batch job systems Deadline: latest expected completion time Soft deadline Timeslot specification is optional 8
9 QoS Execution Modes Provide various strictness levels in meeting QoS targets Strict: Rigid implied throughput and deadline requirements Resources and timeslot must be strictly reserved Elastic(X): Rigid deadline requirement Can tolerate throughput deviation (X% max slowdown) Opportunistic: Strong Flexible throughput and deadline requirements Weak 9
10 Strict Mode Downgrade Manual mode downgrade Requires users to change a job s mode to weaker ones Users fully aware of the consequences Automatic mode downgrade Transparent to users Deadlines are preserved Throughput variation tolerable by jobs Elastic(X) ta X ( td ta) tw = tw tw td 10
11 Strict Opp Mode Downgrade Manual mode downgrade Requires users to change a job s mode to weaker ones Users fully aware of the consequences Automatic mode downgrade Transparent to users Deadlines are preserved Throughput variation tolerable by jobs Elastic(X) ta X ( td ta) tw = tw td-tw tw td 11
12 Strict Opp Impact of Mode Downgrade 4-core CMP receives jobs. 6 jobs are illustrated. Elastic(X) Max Wall-clock time deadline 40% of $ External Resource Fragmentation 20% cache and 2 cores are unused Insufficient resources to accept a new job Internal Resource Fragmentation Job does not use all allocated resources 0T 1T 2T 3T 12
13 Impact of Mode Downgrade 4-core CMP receives jobs. 6 jobs are illustrated. Max Wall-clock time deadline 40% of $ External Resource Fragmentation Internal Resource Fragmentation Strict Opp Elastic(X) 0T 1T 2T 3T 13
14 Impact of Mode Downgrade 4-core CMP receives jobs. 6 jobs are illustrated. Max Wall-clock time deadline 40% of $ External Resource Fragmentation Internal Resource Fragmentation Strict Opp Elastic(X) 0T 1T 2T 3T 14
15 Cache Capacity Partitioning Based on a fine-grain per-set partition scheme [Iyer04][Nesbit07] Job specifies number of cache ways Steal cache capacity = steal cache ways 15
16 Resource Stealing (RS) Overview In Elastic(X), X = maximum CPI increase Must know CPI with and without RS Only know one of them at any given time Observation: CPI components are additive CPI + = CPI L2 > h m = L2 miss per instruction t m = average L2 miss latency t m can be kept constant Resource stealing only changes h m h m increase X% => CPI increase < X% h m t m 16
17 Resource Stealing (RS) Overview In Elastic(X), X = maximum CPI increase Must know CPI with and without RS Only know one of them at any given time Observation: CPI components are additive CPI + = CPI L2 > h m = L2 miss per instruction t m = average L2 miss latency t m can be kept constant Resource stealing only changes h m Monitored through duplicate tags h m increase X% => CPI use increase original partitions < X% h m t m 17
18 Evaluation Methodology Simulation environment (based on Simics) 4-core CMP running Fedora Core 4 Linux 2MB 16-way shared L2 cache Applications Selected from 15 SPEC2006 C/C++ benchmarks bzip2 (Highly cache sensitive) hmmer (Moderately cache sensitive) gobmk (Not cache sensitive) 18
19 Evaluation Methodology Workload composition Workload 1: Ten identical jobs Workload 2: Ten mixed jobs Tight deadline (5 jobs), moderate deadline (3 jobs) and relaxed deadline (2 jobs) Execution mode configurations All-Strict (Base) Hybrid: 4 Strict + 3 Elastic(5%) + 3 Opportunistic jobs All-Strict+AutoDown: 10 Strict jobs Opportunistic EqualPart: Cache equally partitioned among cores No admission control 19
20 Impact of Different Modes Fraction of Strict/Elastic(X) jobs that meet deadlines 100% 80% 60% gobmk hmmer bzip2 40% 20% 0% All-Strict Hybrid All Strict+ AutoDown EqualPart EqualPart: most jobs miss deadlines Our schemes: all Strict/Elastic(X) jobs meet deadlines 20
21 Impact of Different Modes Overall job throughput Normalized throughput gobmk hmmer bzip2 All-Strict Hybrid All Strict+ AutoDown EqualPart Strong QoS throughput trade-offs Execution mode variety boosts throughput Auto mode downgrade transparently boosts throughput Moderate/Relaxed deadlines help 21
22 Resource Stealing Impact of performance slack X in Hybrid case (bzip2) Average CPI or Miss Rate Increase of Elasic(X) jobs 25% 20% 15% 10% 5% 0% MissRate Bound CPI 5% 10% 15% 20% Performance Slack X Using duplicate tags is effective CPI increase < miss rate increase Miss rate is a safe proxy 22
23 Mixed-Benchmark Workloads Strict Elastic(5%) Opportunistic Mix-1 hmmer gobmk bzip2 Mix-2 hmmer bzip2 gobmk Mix-1 favorable for resource stealing bzip2 (cache sensitive) is the recipient gobmk (cache insensitive) is the donor Mix-2 not favorable for resource stealing 23
24 Mixed-Benchmark Workloads Overall throughput Normalized Throughput Mix-1 Mix-2 All-Strict Hybrid All Strict+ AutoDown EqualPart In Hybrid, Mix-1 outperforms Mix-2 Mix-1 can boost throughput up to 46% 24
25 Conclusions Appropriate QoS target? Resource Usage Metrics (RUM) Allowing admission control policy Strong QoS throughput trade-offs Throughput can be safely boosted Significantly (13-46%) Through execution mode downgrade Manually Automatically (transparent to users) Resource stealing effective 25
26 Thank You! Presenter: Fei Guo 26
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