Presentation on theme: "Understanding I/O performance of data intensive astronomy applications with Lustre monitoring tools Gabriele Paciucci, High Performance Data Division Intel."— Presentation transcript:
Understanding I/O performance of data intensive astronomy applications with Lustre monitoring tools Gabriele Paciucci, High Performance Data Division Intel Corporation
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
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. The challenge of monitoring Lustre
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. Metrics and Tools
python/matplotlib Matplotlib.org matplotlib is a python 2D plotting library which produces 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. Tools [oss]# collectl –scdl –i 3 # #cpu sys inter ctxsw KBRead Reads KBWrit Writes KBRead Reads KBWrit Writes [client]# collectl -sl --lustopts R –oTm # #Time KBRead Reads KBWrite Writes Hits Misses 12:20: :20: :20: 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()
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 Intel Manager for Lustre
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 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 IML details
Agenda Lustre* metrics and tools Analytics and presentation Conclusion 11
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 n 2 for a problem of size n (n is the number of pixels in the map). It also has a communication phase that scales as n 3. Analytics
Intel Cloud Edition for Lustre* 13 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.
ICEL for MADBench2 – Scenario 1 We used 256 cores distributed on 32 compute nodes each with 8 cores Intel Xeon E The total available memory was 960 GB. 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) 14 MDS EBS Optimized RAID0 8x 40GB Standard M3.2xlarge OSS EBS Optimized 8x 40GB Standard M3.2xlarge Compute MGS M1.medium OSS EBS Optimized 8x 40GB Standard M3.2xlarge 16x OSS 32x Clients M3.2xlarge 1Gbps
Analytics – Scenario 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) 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. 16 MDS EBS Optimized RAID0 8x 40GB Standard M3.2xlarge OSS EBS Optimized 8x 40GB Standard M3.2xlarge Compute Cc2.8xlarge MGS M1.medium OSS EBS Optimized 8x 40GB Standard M3.2xlarge 16x OSS 16x Clients 10GbE 1Gbps
Analytics – Scenario sec 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) 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 forward- looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand could be different from Intel's expectations due to factors including changes in business and economic conditions; customer acceptance of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and market acceptance of Intel's products; actions taken by Intel's competitors, including product offerings and introductions, marketing programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological developments and to incorporate new features into its products. The gross margin percentage could vary significantly from expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and intangible assets. Intel's results could be affected by adverse economic, social, political and physical/infrastructure conditions in countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters, infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intel's products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures. Intel's results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as the litigation and regulatory matters described in Intel's SEC reports. An unfavorable ruling could include monetary damages or an injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most recent reports on Form 10-Q, Form 10-K and earnings release. Rev. 7/17/13
Virtual hardware available in ICEL Amazon EC2 instances: Spot Instances EBS optimized High network capabilities Amazon EBS storage: Networked storage Max size 1TB per EBS volume Not magic Standard, not Provisioned in ICEL 21 VMs sizevCP U Intel CPUvRAM (GB) EBS M3.2xlarge8Intel Xeon E Yes CC2.8xlarg e 32Intel Xeon E N/A EBS StorageIOPSSizePerformance (WRITE) ** StandardN/A MB/sec Provisioned MB/sec Provisioned MB/sec ** not intended to be authoritative numbers Network performance ** M3.2xlargeCC2.8xlarge M3.2xlarge1.01 Gbps1.87 Gbps CC2.8xlarge1.87 Gbps6.18 Gbps
Analytics – Scenario 1 Linear scale Log scale The MADbench application does appear to scale as n 3 for larger instances, though not necessarily for the smaller ones. This is what we expected. But the scaling study doesn’t illuminate why.
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.