Revisiting Memory Errors in Large-Scale Production Da Centers Analysis and Modeling of New Trends from the Field Justin Meza Qiang Wu Sanjeev Kumar Onur Mutlu
Overview Study of DRAM reliability: on modern devices and workloads at a large scale in the field
Overview Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Overview Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Errors follow a power-law distribution and a large number of errors occur due to sockets/channels Modeling errors Architecture & workload
Overview Error/failure occurrence We find that newer cell fabrication technologies have higher failure rates Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Overview Error/failure occurrence Chips per DIMM, transfer width, and workload type (not necessarily CPU/ memory utilization) affect reliability Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Overview Error/failure occurrence We have made publicly available a stistical model for assessing server memory reliability Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Overview Error/failure occurrence Page offlining at scale First large-scale study of page offlining; real-world limitions of technique New reliability trends Technology scaling Modeling errors Architecture & workload
Outline background and motivation server memory organization error collection/analysis methodology memory reliability trends summary
Background and motivation
DRAM errors are common examined extensively in prior work charged particles, wear-out variable retention time (next lk) error correcting codes used to detect and correct errors require additional storage overheads
Our goal Strengthen undersnding of DRAM reliability by studying: new trends in DRAM errors modern devices and workloads at a large scale billions of device-days, across 4 months
Our main contributions identified new DRAM failure trends developed a model for DRAM errors evaluated page offlining at scale
Server memory organization
Socket
Memory channels
DIMM slots
DIMM 24 2
Chip 24 2
Banks 24 2
24 2 Rows and columns
24 2 Cell
User da 24 2 ECC meda additional 2.% overhead
Reliability events Fault the underlying cause of an error DRAM cell unreliably stores charge Error the manifestion of a fault permanent: every time transient: only some of the time
Error collection/ analysis methodology
DRAM error measurement measured every correcble error across Facebook's fleet for 4 months meda associated with each error parallelized Map-Reduce to process used R for further analysis
System characteristics 6 different system configurations Web, Hadoop, Ingest, Dabase, Cache, Media diverse CPU/memory/storage requirements modern DRAM devices DDR communication protocol (more aggressive clock frequencies) diverse organizations (banks, ranks,...) previously unexamined characteristics density, # of chips, transfer width, workload
Memory reliability trends
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Server error rate )UaFtion of seuveus.4...2.2.... CoUUeFEle euuous (C() 8nFoUUeFEle euuous (8C() % 7/ 8/ 9/ / / 2/ /4 2/4 /4 4/4 /4 6/4 7/4 8/4.% onth
Memory error distribution
Memory error distribution creasing hazard rate
Memory error distribution creasing hazard rate How are errors mapped to memory organization?
Sockets/channels: many errors Socket Channel Bank Row Column Cell Spurious Fraction of errors...
Sockets/channels: many errors Fraction of errors... Socket Channel Bank Row Not mentioned in prior chiplevel studies Column Cell Spurious
Sockets/channels: many errors Fraction of errors... Socket Channel Bank Row Not accounted for in prior chiplevel studies At what rate do components fail on servers? Column Cell Spurious
Bank/cell/spurious failures are common Socket Channel Bank Row Column Cell Spurious Fraction of servers...
# of servers # of errors Socket Channel Fraction of servers... Fraction of errors Socket Channel... nial-ofservice like behavior
# of servers # of errors Socket Channel Fraction of servers... Socket Fraction of errors Channel... nial-ofservice like behavior What factors contribute to memory failures at scale?
Analytical methodology measure server characteristics not feasible to examine every server examined all servers with errors (error group) sampled servers without errors (control group) bucket devices based on characteristics measure relative failure rate of error group vs. control group within each bucket
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Prior work found inconclusive trends with respect to memory capacity Relative server failure rate... 2 8 6 24 DIMM capacity (GB)
Prior work found inconclusive trends with respect to memory capacity Relative server failure rate... Examine characteristic more closely related to cell fabrication technology 2 8 6 24 DIMM capacity (GB)
Use DRAM chip density to examine technology scaling (closely related to fabrication technology)
Relative server failure rate... 2 4 Chip density (Gb)
Relative server failure rate... Intuition: quadratic increase in capacity 2 4 Chip density (Gb)
Error/failure occurrence We find that newer cell fabrication technologies have higher failure rates Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
DIMM architecture chips per DIMM, transfer width 8 to 48 chips x4, x8 = 4 or 8 bits per cycle electrical implications D Registe Clock 47. 7. il C. il D Back 2..2 Side 24 2 f Con B.46mm m il of Con C il of Con D.8. 2..~.....8.. 2..2
DIMM architecture Does DIMM organization affect memory reliability? chips per DIMM, transfer width 8 to 48 chips x4, x8 = 4 or 8 bits per cycle electrical implications D Registe Clock 47. 7. il C. il D Back 2..2 Side 24 2 f Con B.46mm m il of Con C il of Con D.8. 2..~.....8.. 2..2
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 No consistent trend across only chips per DIMM x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 More chips higher failure rate 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 More bits per cycle higher failure rate 8 6 2 48 Da chips per DIMM
Gb 2 Gb 4 Gb Relative server failure rate... x8 x4 Intuition: increased electrical loading 8 6 2 48 Da chips per DIMM
Workload dependence prior studies: homogeneous workloads web search and scientific warehouse-scale da centers: web, hadoop, ingest, dabase, cache, media
Workload dependence prior studies: homogeneous workloads What affect to heterogeneous workloads have on reliability? web search and scientific warehouse-scale da centers: web, hadoop, ingest, dabase, cache, media
Gb 2 Gb 4 Gb Relative server failure rate... Relative server failure rate....2..7 CPU utilization.2..7 Memory utilization
Gb 2 Gb 4 Gb Relative server failure rate... Relative server failure rate No consistent trend across CPU/memory utilization....2..7 CPU utilization.2..7 Memory utilization
Web Hadoop Ingest Dabase Memcache Media Relative server failure rate...
Web Hadoop Ingest Dabase Memcache Media Relative server failure rate... Varies by up to 6.x
Error/failure occurrence Chips per DIMM, transfer width, and workload type (not necessarily CPU/ memory utilization) affect reliability Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
A model for server failure use stistical regression model compare control group vs. error group linear regression in R trained using da from analysis enable exploratory analysis high perf. vs. low power systems
nsity Chips Age Memory error model Relative server failure rate...
nsity Chips Age Memory error model Relative server failure rate... Number of physical CPU cores in the server. 2 Yes 2 26 449 ln ŒF=. F/ç D ˇIntercept C.Capacity ˇCapacity / C.nsity2Gb ˇnsity2Gb / C.nsity4Gb ˇnsity4Gb / C.Chips ˇChips / C.CPU% ˇCPU% / C.Age ˇAge / C.CPUs ˇCPUs /
Available online http://www.ece.cmu.edu/~safari/tools/memerr/
Error/failure occurrence We have made publicly available a stistical model for assessing server memory reliability Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Prior page offlining work [Tang+,DSN'6] proposed technique "retire" faulty pages using OS do not allow software to allocate them [Hwang+,ASPLOS'2] simulated eval. error traces from Google and IBM recommended retirement on first error large number of cell/spurious errors
Prior page off lining work [Tang+,DSN'6] proposed technique "retire" faulty pages using OS How effective is page offlining in the wild? do not allow software to allocate them [Hwang+,ASPLOS'2] simulated eval. error traces from Google and IBM recommended retirement on first error large number of cell/spurious errors
Cluster of 2,276 servers
Cluster of 2,276 servers -67%
Cluster of 2,276 servers -67% Prior work: -86% to -94%
Cluster of 2,276 servers -67% 6% of page offlining attempts failed due to OS Prior work: -86% to -94%
Error/failure occurrence Page offlining at scale First large-scale study of page offlining; real-world limitions of technique New reliability trends Technology scaling Modeling errors Architecture & workload
Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
More results in paper Vendors Age Processor cores Correlation analysis Memory model case study
Summary
Modern systems Large scale
Summary Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Summary Error/failure occurrence Page offlining at scale New reliability trends Technology scaling Errors follow a power-law distribution and a large number of errors occur due to sockets/channels Modeling errors Architecture & workload
Summary Error/failure occurrence We find that newer cell fabrication technologies have higher failure rates Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Summary Error/failure occurrence Chips per DIMM, transfer width, and workload type (not necessarily CPU/ memory utilization) affect reliability Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Summary Error/failure occurrence We have made publicly available a stistical model for assessing server memory reliability Page offlining at scale New reliability trends Technology scaling Modeling errors Architecture & workload
Summary Error/failure occurrence Page offlining at scale First large-scale study of page offlining; real-world limitions of technique New reliability trends Technology scaling Modeling errors Architecture & workload
Revisiting Memory Errors in Large-Scale Production Da Centers Analysis and Modeling of New Trends from the Field Justin Meza Qiang Wu Sanjeev Kumar Onur Mutlu
Backup slides
creasing hazard rate
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A n Co of. il Errors. : te No. nsity..8 B ts c on fc il o et llim mi s: t i Un.4.9 4 c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle 4Gb. ted s ise w r e s er D.8 B ts c on fc il o. et llim mi s: t i Un.4.9 4 f C s er. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle Gb 9. 7.. 2.. x ma.. 4 2. D A n Co of. il il o e.8.6.2.2 B ts c on fc il o C. et llim mi s: t i Un.4.9 4 f n Co 2. 2. mm 7.2. 7.2. ted s ise w r e ~. on fc. s er 2. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle 2Gb Buckets Errors nsity..6. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2. ~. mm 7.2 x ma. et llim mi s: t i Un 7.2. ted s ise w r e.. il o e n Co o il D. x ma.. 4 2. A n Co of. il on fc 6m.4 D 2. mm 7.2. ring r iste Reg k Drive Cloc ring r iste Reg k Drive Cloc o il. 7.6 9. 7.. 2. 9. 7.. 2. P_ W :WP_ 89 _482 CWM APCP D ~. e Sid m max 6m.4.8 f C 7..2 D il o e n Co.2 4,26.. 4 2. A n Co of. il.2 x ma e Sid m max.. X 2 2. 2....8. on fc C il. 47 2 ck Ba 2.. 6m.4 2. mm 7.2..6 4X il D.2.4.9 4 ~..2..2.8 B ts c on fc il o 7. o il 2. D 24 f C.. 7 2. 2 il A e Sid m max.. X D 2. il o e 6m.4 on fc C il. 47. 2. 7. o il n Co 2.. 2. ck Ba. 4X il D.8 2.. X. e Sid m max 7. il A 9 8. TS 2 SPD il B.2 2.. 2.. X C il. 47 2. ck Ba 2.. il D nt Fro.. 7 4X 2 x7 G il B. 7 A R8 BF 2 R7 G MT -H.2. 9 8. TS 2 SPD.2 il A 2. C il. 47. nt Fro. P_ W :WP_ 89 _482 CWM APCP.. 2. A R8 BF 2 R7 G MT -H.. 2 x7 G il B. 7 il D ck Ba 9 8. TS 2 SPD.8 4X..2 il A nt Fro. P_ W :WP_ 89 _482 CWM APCP 2. ring r iste Reg k Drive Cloc 2 x7 G il B A R8 BF 2 R7 G MT -H.2 9 8. TS 2 SPD ring r iste Reg k Drive Cloc. nt Fro..2 2. P_ W :WP_ 89 _482 CWM APCP P_ W :WP_ 89 _482 CWM APCP 2 x7 G :WP_ 89 _482 CWM APCP 9. 7.. 2. P_ W A R8 BF R7 G MT -H P_ W :WP_ 89 _482 CWM APCP P_ W :WP_ 89 _482 CWM APCP oth ss nle. ted s ise w r e 2Gb s er 7
A n Co of. il Errors. : te No. nsity..8 B ts c on fc il o et llim mi s: t i Un.4.9 4 c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle 4Gb. ted s ise w r e s er D.8 B ts c on fc il o. et llim mi s: t i Un.4.9 4 f C s er. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle Gb 9. 7.. 2.. x ma.. 4 2. D A n Co of. il il o e.8.6.2.2 B ts c on fc il o C. et llim mi s: t i Un.4.9 4 f n Co 2. 2. mm 7.2. 7.2. ted s ise w r e ~. on fc. s er 2. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle 2Gb Errors nsity..6. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2. ~. mm 7.2 x ma. et llim mi s: t i Un 7.2. ted s ise w r e.. il o e n Co o il D. x ma.. 4 2. A n Co of. il on fc 6m.4 D 2. mm 7.2. ring r iste Reg k Drive Cloc ring r iste Reg k Drive Cloc o il. 7.6 9. 7.. 2. 9. 7.. 2. P_ W :WP_ 89 _482 CWM APCP D ~. e Sid m max 6m.4.8 f C 7..2 D il o e n Co.2 4,26.. 4 2. A n Co of. il.2 x ma e Sid m max.. X 2 2. 2....8. on fc C il. 47 2 ck Ba 2.. 6m.4 2. mm 7.2..6 4X il D.2.4.9 4 ~..2..2.8 B ts c on fc il o 7. o il 2. D 24 f C.. 7 2. 2 il A e Sid m max.. X D 2. il o e 6m.4 on fc C il. 47. 2. 7. o il n Co 2.. 2. ck Ba. 4X il D.8 2.. X. e Sid m max 7. il A 9 8. TS 2 SPD il B.2 2.. 2.. X C il. 47 2. ck Ba 2.. il D nt Fro.. 7 4X 2 x7 G il B. 7 A R8 BF 2 R7 G MT -H.2. 9 8. TS 2 SPD.2 il A 2. C il. 47. nt Fro. P_ W :WP_ 89 _482 CWM APCP.. 2. A R8 BF 2 R7 G MT -H.. 2 x7 G il B. 7 il D ck Ba 9 8. TS 2 SPD.8 4X..2 il A nt Fro. P_ W :WP_ 89 _482 CWM APCP 2. ring r iste Reg k Drive Cloc 2 x7 G il B A R8 BF 2 R7 G MT -H.2 9 8. TS 2 SPD ring r iste Reg k Drive Cloc. nt Fro..2 2. P_ W :WP_ 89 _482 CWM APCP P_ W :WP_ 89 _482 CWM APCP 2 x7 G :WP_ 89 _482 CWM APCP 9. 7.. 2. P_ W A R8 BF R7 G MT -H P_ W :WP_ 89 _482 CWM APCP P_ W :WP_ 89 _482 CWM APCP oth ss nle. ted s ise w r e 2Gb s er 7
A n Co of. il Errors. : te No. nsity..8 B ts c on fc il o et llim mi s: t i Un.4.9 4 c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle 4Gb. ted s ise w r e s er D.8 B ts c on fc il o. et llim mi s: t i Un.4.9 4 f C s er. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle Gb 9. 7.. 2.. x ma.. 4 2. D A n Co of. il il o e.8.6.2.2 B ts c on fc il o C. et llim mi s: t i Un.4.9 4 f n Co 2. 2. mm 7.2. 7.2. ted s ise w r e ~. on fc. s er 2. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2 oth ss nle 2Gb Errors nsity..6. : te No. c ran tole Re v.. eo /A na ug u ns sio en im ll d.2. ~. mm 7.2 x ma. et llim mi s: t i Un 7.2. ted s ise w r e.. il o e n Co o il D. x ma.. 4 2. A n Co of. il on fc 6m.4 D 2. mm 7.2. ring r iste Reg k Drive Cloc ring r iste Reg k Drive Cloc o il. 7.6 9. 7.. 2. 9. 7.. 2. P_ W :WP_ 89 _482 CWM APCP D ~. e Sid m max 6m.4.8 f C 7..2 D il o e n Co.2 4,26.. 4 2. A n Co of. il.2 x ma e Sid m max.. X 2 2. 2....8. on fc C il. 47 2 ck Ba 2.. 6m.4 2. mm 7.2..6 4X il D.2.4.9 4 ~..2..2.8 B ts c on fc il o 7. o il 2. D 24 f C.. 7 2. 2 il A e Sid m max.. X D 2. il o e 6m.4 on fc C il. 47. 2. 7. o il n Co 2.. 2. ck Ba. 4X il D.8 2.. X. e Sid m max 7. il A 9 8. TS 2 SPD il B.2 2.. 2.. X C il. 47 2. ck Ba 2.. il D nt Fro.. 7 4X 2 x7 G il B. 7 A R8 BF 2 R7 G MT -H.2. 9 8. TS 2 SPD.2 il A 2. C il. 47. nt Fro. P_ W :WP_ 89 _482 CWM APCP.. 2. A R8 BF 2 R7 G MT -H.. 2 x7 G il B. 7 il D ck Ba 9 8. TS 2 SPD.8 4X..2 il A nt Fro. P_ W :WP_ 89 _482 CWM APCP 2. ring r iste Reg k Drive Cloc 2 x7 G il B A R8 BF 2 R7 G MT -H.2 9 8. TS 2 SPD ring r iste Reg k Drive Cloc. nt Fro..2 2. P_ W :WP_ 89 _482 CWM APCP P_ W :WP_ 89 _482 CWM APCP 2 x7 G :WP_ 89 _482 CWM APCP 9. 7.. 2. P_ W A R8 BF R7 G MT -H P_ W :WP_ 89 _482 CWM APCP P_ W :WP_ 89 _482 CWM APCP oth ss nle. ted s ise w r e 2Gb s er 7
Case study
Case study Factor Low-end High-end (HE) Capacity 4 GB 6 GB nsity2gb nsity4gb Chips 6 2 CPU% % 2% Age CPUs 8 6 Predicted relative failure rate.2.78 Inputs Output
Case study Factor Low-end High-end (HE) Capacity 4 GB 6 GB nsity2gb nsity4gb Chips a higher 6 impact? 2 CPU% % 2% Age CPUs 8 6 Predicted relative failure rate.2.78 Does CPUs or density have Inputs Output
Exploratory analysis Factor Low-end High-end (HE) HE/#density HE/#CPUs Capacity 4 GB 6 GB 4 GB 6 GB nsity2gb nsity4gb Chips 6 2 6 2 CPU% % 2% 2% % Age CPUs 8 6 6 8 Predicted relative failure rate.2.78..
Exploratory analysis Factor Low-end High-end (HE) HE/#density HE/#CPUs Capacity 4 GB 6 GB 4 GB 6 GB nsity2gb nsity4gb Chips 6 2 6 2 CPU% % 2% 2% % Age CPUs 8 6 6 8 Predicted relative failure rate.2.78..