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Michael Kluge, ZIH
Correlating Multiple TB
of Performance Data to User Jobs
Lustre User Group 2015, Denver, Colorado
Zellescher Weg 12
Willers-Bau A 208
Tel. +49 351 - 463 – 34217
Michael Kluge ([email protected])
About us
ZIH: about us
– HPC and service provider
– Research Institute
– Big Data Competence Center
Main research areas:
– Performance Analysis Tools
– Energy Efficiency
– Computer Architecture
– Standardization Efforts (OpenACC, OpenMP)
Michael Kluge, ZIH
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ZIH HPC and BigData Infrastructure
Erlangen, Potsdam
100 Gbit/s Cluster Uplink
TU
Dresden
ZIHBackbone
Home
Archiv
HTW, Chemnitz, Leipzig, Freiberg
Parallel Machine
BULL HPC
Haswell processors
2 x 612 Nodes
~ 30.000 Cores
other clusters
Megware Cluster
IBM iDataPlex
High Throughput
BULL Islands
Westmere, Sandy Bridge,
und Haswell processors,
GPU-Nodes
700 Nodes
~15.000 Cores
Shared Memory
SGI Ultraviolet 2000
512 cores SandyBridge
8 TB RAM NUMA
Storage
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HRSK-II, Phase 2, currently installed
GPU Isle
64 nodes
Haswell
2 x 12 cores
64 GB RAM
+
2 x Nvidia K
80
Troughput Isle
232 nodes
Haswell
2 x 12 cores
64/128/256
GB RAM
128 GB SSD
HPC Isle
612 nodes
Haswell
2 x 12 cores
64 GB RAM
128 GB SSD
HPC Isle
612 nodes
Haswell
2 x 12 cores
64 GB RAM
128 GB SSD
2x
Export
2 x SMP nodes
2 TB RAM, 48 cores
64 GB RAM
24 Lustre Server
5 PB
NLSATA
80 GB/s
400.000
IOPS
40 TB
SSD
10 GB/s
1 Mio.
IOPS
4x
Login
to Campus 4 x 10GE
Uplink, Home, Backup
~ 1,2 PF peak in CPUs, ~35.000 cores total
~ 300 TF peak in GPUs, 128 cards
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FDR
InfiniBand
Architecture of our storage concept (FASS)
ZIH-Infrastructure
Flexible Storage System (FASS)
Login
…
User Z
Transaction
SCRATCH
User A
Storage
SSD
Server 2
Server N
Server/File systems
User A
User Z
Net
Switch 2
HPC-Component
Switch 1
Export
Batch-System
Checkpoint.
Scratch
Server 1
…
SAS
SATA
Throughput-Component
Optimization and control
Michael Kluge, ZIH
Access
statistics
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Analysis
Performance Analysis for I/O Activities (1)
What is of interest
– Application requests, Interface types
– Access sizes/patterns
– Network/Server/Storage utilization
Level of detail:
– Record everything
– Record data every 5 to 10 seconds
Challenge:
– How to analyze this?
– How to deal with anonymous data?
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I/O and Job Correlation: Architecture/Software
SLURM Batch System
HPC File System
node
MDS
node
I/O
network
node
RAID
Batch System
Log Files
Global I/O Data
OSS
RAID
node
OSS
SAN
Cluster Formation
RAID
application events
node local events
Correlation System
OSS
utilization
errors
Job Clusters
backend, cache
throughput, IOPS
Job Clusters
with high
I/O demands
Agent
Interface
Agent
Interface
Agent
Interface
Agent
Interface
Data Processing
Database
Storage
Database
Storage
Job Clusters
with low
I/O demands
Database
Storage
Data
Request
s
Dataheap Data Collection System
https://fusionforge.zih.tu-dresden.de/projects/dataheap/
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Visualization of Live Data for Phase 1
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Visualization of Live Data for Phase 2
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Performance Analysis for I/O Activities (2)
Amount of collected data:
– 240 OSTs, 20 OSS servers, …
– Looking at stats and brw_stats (how to store a histogram in
database?)
 ~75.000 tables
– about 1 GB of data per hour
Analysis is supposed to cover 6 month
– 4 TB data
– No way to analyze this with any serial approach
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Analysis Process
map operations to a set of tables
– modify all tables (align data, scale, aggregate over time)
– create a new table from an input set of tables (aggregation)
– plot (time series, histograms)
currently: single thread Haskell program
Swift/T work in progress
– 4 TB means 50 seconds on an 80 GB/s file system for the
initial read
– Swift/T has nice ways to describe data dependencies and to
parallelize this kind of workflows (open for suggestions!)
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Approach to look at the Raw Data
analysis of the raw performance data
– take original data and create derived metrics
– look at plots and histograms
correlate metrics with user jobs
– take job log and split jobs into classes
– check for correlations between job classes and
performance metrics
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Metric Example: NetApp vs. OST Bandwidth (20s)
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Metric Example: NetApp vs. OST Bandwidth (60s)
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Metric Example: NetApp vs. OST Bandwidth (10 minutes)
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Metric Example: 3 Month bandwidth vs. IOPS
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I/O and Job Correlation: Starting Point (Bandwidth)
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Building Job Classes
a class is defined by:
– user id, core count
– similar runtime
– similar start time
(time isolation)
sort all jobs by runtime, split list at gaps
split these groups again if they are not overlapping
(long gap between start)
challenges: runtime fluctuation, large run time variations
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I/O and Job Correlation: Preliminary Result (1)
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I/O and Job Correlation: Preliminary Result (1)
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I/O and Job Correlation: Correlation Factors
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Open Topics
How to store a histogram in database?
Other approaches than Swift/T
Deal with the zeros
Cache intermediate results
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Conclusion / Questions
it is possible to map anonymous performance
data back to user jobs
advance towards storage controller analysis
(caches, backend bandwidth, …)
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