Understanding I/O performance of data intensive astronomy applications with Lustre monitoring tools Gabriele Paciucci, High Performance Data Division Intel Corporation Legal Disclaimer INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. A "Mission Critical Application" is any application in which failure of the Intel Product could result, directly or indirectly, in personal injury or death. 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Agenda • Lustre* metrics and tools • Analytics and presentation • Conclusion 3 Why Monitor Lustre*? • With the exponential growth of high-fidelity sensor and simulated data, the scientific community is increasingly reliant on Exascale HPC resources to handle their data analysis requirements. • Lustre is the leading parallel file system in the Exascale Era. • However, to utilize all the Lustre power effectively, the I/O components must be designed in a proper way, as any architectural bottleneck will quickly render the platform inefficient. 4 The challenge of monitoring Lustre • Understanding the Lustre metrics in the proc filesystem, gives the opportunity to Administrators to design a Lustre cluster and maintain the performance requested. • But there are thousand of metrics in a mid-size Lustre file system for each components that include clients, servers and Lustre networks These components are distributed: a problem on one node can affect multiple nodes and finding the initial source of a problem can be difficult without an integrated monitor tool. Metrics and Tools • What you monitor will depend on what you want to know and on what you think the problems are. • What you want to measure will also guide your choice of tools for collecting, analyzing, and presenting the data. Tools python/matplotlib Matplotlib.org matplotlib is a python 2D plotting library which produces plt.xlabel('time') plt.ylabel(r'$MiB/sec$') plt.setp( ax.get_xticklabels(), rotation=30, horizontalalignment='right') plt.title("%s on AWS %s Aggregate OST data rates" % (self.application, dayStr)) plt.legend() plt.savefig(plot) plt.cla() publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell collectl collectl.sourceforge.net CollectL is a tool that can be used to monitor Lustre. You can run CollectL on a Lustre system that has any combination of MDSs, OSTs and clients. The collected data can be written to a file for continuous logging and played back at a later time. It can also be converted to a format suitable for plotting. LMT github.com/chaos/lmt/wiki The Lustre Monitoring Tool (LMT) monitors Lustre File System servers (MDT, OST, and LNET routers). It collects data using the Cerebro monitoring system and stores it in a MySQL database. Graphical and text clients are provided which display historical and real time data pulled from the database. [oss]# collectl –scdl –i 3 #<--------CPU--------><----------Disks-----------><---------Lustre OST---------> #cpu sys inter ctxsw KBRead Reads KBWrit Writes KBRead Reads KBWrit Writes 19 19 1930 563 0 0 27211 251 0 0 28701 28 9 8 1346 239 0 0 17269 165 0 0 9225 9 [client]# collectl -sl --lustopts R –oTm # <---------------Lustre Client---------------> #Time KBRead Reads KBWrite Writes Hits Misses 12:20:50.003 17138 8 12854 13 4100 0 12:20:51.002 18450 9 20500 20 4349 0 12:20:52.003 32735 16 20460 20 8447 0 Intel Manager for Lustre • Bundled with Intel Enterprise Edition for Lustre • Simplified installation, configuration, monitoring and management of Lustre • Provides plugin interface for integration with storage and other software tools • Storage hardware neutral • Intuitive GUI • Fully featured CLI IML details Monitors • Read and write throughput to the file system • Metadata operations to the file system • CPU and RAM usage on MDS and OSS • Delve down to individual server and individual Lustre targets • Aggregate system log of all of Lustre servers • The health of Lustre targets and servers • LNET status • The number of clients connected to the Lustre file system • The usage of Lustre file system Manages • Install Lustre and IML related packages • Automatically setup High Availability • Power control Lustre servers • power down, on, cycle • Manual failover and failback option • Create & Setup new Lustre file systems • Manage multiple Lustre file systems • Rescan network configuration changes • Re-configure Lustre file systems • Support via GUI or CLI IML dashboard Agenda • Lustre* metrics and tools • Analytics and presentation • Conclusion 11 Analytics http://crd-legacy.lbl.gov/~borrill/cmb/madcap/ http://crd-legacy.lbl.gov/~borrill/MADbench2/ • In the MADbench2 application the problem is to generate simulations of the cosmic microwave background radiation sky map. • Each of those simulations involves a very large matrix inversion that is solved with an out-of-core algorithm (thus the I/O) • That phase of the application can be I/O intensive and scales as n2 for a problem of size n (n is the number of pixels in the map). It also has a communication phase that scales as n3. Intel Cloud Edition for Lustre* • We have used Intel Cloud Edition for Lustre to understand how the application scales, what its workload looks like and how we can size the environment to maximize the results • Intel Cloud Edition for Lustre* or ICEL is a scalable, shared filesystem for HPC applications in the cloud • AWS allows you to run Lustre on an Amazon Machine Image (AMI). • The Intel Lustre AMI is designed to be used with a CloudFormation template that defines all the resources needed by the Lustre file system. 13 ICEL for MADBench2 – Scenario 1 We used 256 cores distributed on 32 compute nodes each with 8 cores Intel Xeon E5-2670. The total available memory was 960 GB. MGS M1.medium RAID0 8x 40GB Standard 32x Clients M3.2xlarge The RAW space for the Lustre file system is 5TB. The max sequential performance is limited by the OSS’s network to 16 Gbps (16x1Gbps) MDS EBS Optimized Compute 8x 40GB Standard OSS EBS Optimized M3.2xlarge M3.2xlarge 16x OSS 8x 40GB Standard OSS EBS Optimized 1Gbps M3.2xlarge 14 Analytics – Scenario 1 519.51 sec • The smaller instances ran quickly (not shown) • The 32k and 64k pixel instances became communication bound • The MADbench2 application reads just as much as it writes • Even at this larger scale the I/O fits entirely in client cache, so the reads do not generate any traffic to the servers (where ltop is listening) 3860.67 sec Changing the compute nodes – Scenario 2 The decision was to improve the inter-process communication using instances with 10GbE. The instances with such networks have 32 cores, so we could cut the number of compute nodes from 32 to 16. We did not modify the size or the performance of the Lustre file system. MGS M1.medium RAID0 8x 40GB Standard MDS EBS Optimized 16x Clients M3.2xlarge Compute 8x 40GB Standard OSS EBS Optimized M3.2xlarge 16x OSS 8x 40GB Standard Cc2.8xlarge 10GbE OSS EBS Optimized M3.2xlarge 1Gbps 16 Analytics – Scenario 2 • The analysis allowed us to decrease the Time To Run the application by 40% increasing the network bandwidth (1Gb to 10GbE) • Reduce the cost to run the application by 50% by lowering the amount of the servers (32 to 16) 310.44 sec 2427.64 sec Conclusion • There is a wealth of information about the health and performance of Lustre* available in the Proc filesystem • Proactively tracking the changes in that information will allow system staff to anticipate and repair problems or make a better design of the cluster and save money • Knowing the tools for gathering, analyzing, and presenting the information will help with system issues and with the impacts of user codes. • In the event that a fault is reported, the monitoring telemetry can help quickly isolate a specific root cause. 18 Risk Factors The above statements and any others in this document that refer to plans and expectations for the third quarter, the year and the future are forwardlooking statements that involve a number of risks and uncertainties. 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Rev. 7/17/13 Virtual hardware available in ICEL VMs size vCP U Intel CPU M3.2xlarge 8 Intel Xeon E5-2670 30 Yes 32 Intel Xeon E5-2670 60 N/A Amazon EC2 instances: • Spot Instances • EBS optimized • CC2.8xlarg High network capabilitiese vRAM (GB) EBS Network performance ** M3.2xlarge CC2.8xlarge Amazon EBS storage: M3.2xlarge 1.01 Gbps 1.87 Gbps • Networked storage CC2.8xlarge 1.87 Gbps 6.18 Gbps • Max size 1TB per EBS volume EBS Storage IOPS Size Performance (WRITE) ** • Not magic Standard N/A 100 24+ MB/sec • Standard, not Provisioned in ICEL Provisioned 2000 200 35+ MB/sec Provisioned 4000 400 50+ MB/sec ** not intended to be authoritative numbers 21 Analytics – Scenario 1 • • • The MADbench application does appear to scale as n3 for larger instances, though not necessarily for the smaller ones. This is what we expected. But the scaling study doesn’t illuminate why. Linear scale Log scale Analytics – Scenario 1 • The ltop utility in the LMT package records OST stat file contents much like llstat. • An ad hoc python script (with numpy and matplotlib) that parses the output recorded by ltop can present a variety of special-case views of the data.