Towards I/O monitoring at scale

Designing a self-tuning I/O environment in HPC

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I/O Challenges in HPC

In High-Performance Computing (HPC) data movements are one of the biggest challenges. Indeed, large computation is necessarily leading to large datasets. Current HPC workflows favor a feed-forward way of launching programs, loading their dataset, and then storing the result in persistent storage for later post-processing. This is done by separate jobs without any form of collaboration. Moreover, the I/O backend is so critical that it generally runs separated from the machine in a service island, being dimensioned for the whole system. What if the I/O subsystem and the application started collaborating to perform better? This is the question ADMIRE tries to respond to. This EUROHPC project has taken the ambitious goal of experimenting with a holistic I/O management approach. In the project’s framework, it translates into a feedback loop and careful job and service reconfiguration to handle I/O resources globally in the computing center. This should translate to a lower dependency on the I/O backend reconfiguring nodes to act as an ad-hoc file-system — reducing the need for large and expensive I/O backplanes. In this context, being able to precisely describe what is taking place on the system is crucial, and to this matter, a new real-time monitoring system was developed by project partners.    

Always on Profiling

ADMIRE intends to develop an always-on monitoring infrastructure to enable malleable jobs. Such undertaking leads to strong constraints on the measurement system which has to provide both real-time and goal feedback on all layers of a supercomputer. These measurements are to be used to guide scheduling decisions and dynamic I/O reconfiguration during job allocation.

Data-Pipeline

Overview of the ADMIRE measurement
infrastructure, storing data in Prometheus

To bridge with existing technologies in the cloud and more generally in IT, we retained Prometheus as our performance database, time-series being the metric of choice to guide ADMIRE’s components’ decision. Our goal is then to provide a whole system’s view in the Prometheus performance database. However, due to the important number of data sources and their evolving number (processes launched in jobs) we had to develop a specifically tailored aggregating push gateway for Prometheus: the tau_metric_proxy.

Aggregating Push-Gateway

ADMIRE aggregating push gateway designed to collect data from hundred of processes

Thanks to this push gateway specifically designed for the project, we can locally aggregate performance data on each node in real time. Prometheus then has to collect only a reduced amount of data from a fixed number of data sources. These developments were combined with the design of specialized exporters for MPI, I/O, and at the node-level. All these data can be fed into the database to enable close to real-time global system view up to the MPI call level. This is an unprecedented level of verbosity that we intend to achieve machine-wide while remaining compatible with Exascale constraints.

Tree-Based Overlay Network

Illustration of a tree-based overlay network (TBON)

Our key approach to maintaining a manageable level of performance data is to combine high-frequency measurements spatially to limit the resulting number of data points. As of today, ADMIRE can collect up to 3000 metrics in real-time on each node. Prometheus aggregates them locally every 5 seconds. Besides, to provide a global view we rely on the LIMITLESS monitoring and aggregation tool with relies on a Tree-Based Overlay Network (TBON), see Fig III, to generate spatially reduced performance data over time for the whole system. Thanks to this multi-tiered approach, we can then maintain high-frequency spatialized performance blaming while preventing a combinatorial explosion when tracking the global system’s state. As a result, we can feed at high-frequency a Prometheus instance without connecting to N individual nodes — dodging a scalability challenge.

The Way Ahead…

Grafana dashboard on the IMB benchmark showing how ADMIRE can capture all MPI calls and I/O events.

The project features a diverse set of representative HPC applications. In a fully-integrated co-design process we intend to validate our experiments on the ADMIRE I/O subsystem in real-world conditions on true applications. We are currently entering our integration phase where all the specifically developed components are being bridged together. On the monitoring side, we have a dedicated deployment helper setting-up the whole measurements infrastructure in user-space and we will leverage it in conjunction with all the other ADMIRE components.

Captured Metrics

ADMIRE’s monitoring infrastructure is capable of collecting a wide range of metrics today and we constantly keep improving the measurement layer to provide a detailed view of the system.

Message Passing Interface

We capture all MPI calls and their duration and respective sizes in an always-on fashion. It means in ADMIRE’s configuration you can see MPI calls rate and throughput in real-time on the whole system.

I/O Monitoring

Thanks to a close collaboration with the I/O backend and dedicated instrumentation layers, ADMIRE can non-intrusively characterize I/O traffic. To do so, we instrumented both common interfaces (POSIX) and specialized (Ad-Hoc, MPI) I/O interfaces to track both the bandwidth and origin of the data traffic.

System Monitoring

System monitoring is done by the LIMITLESS daemons which precisely track node-level data with optimized data-cropping algorithms to only account for representative state changes. Thanks to this component all parameters relative to nodes’ health and global resource usage are tracked in real-time thanks to the reduction Tree-Based Overlay Network (TBON).

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