Abstract
Trident, a qualitative analysis tool that can look at various low level metrics with respect to the Core, Memory and I/O to highlight performance bottlenecks during the execution of an application. Trident uses a three pronged approach in analysing a node's utilisation of hardware resources and to help a non system expert understand the stress on different parts of the system by a given job. Currently metrics such as memory bandwidth, core utilization, active processor cyles, etc., are being collected. Interpretation of the data in raw form is often non intuitive. Therefore, the tool converts these data into derived metrics that are then represented as a system wide extended Top-Down analysis that helps developers and site managers likewise understand the application behavior without the need for in-depth expertise of architecture details.
Highlights
The concept of performance counter monitoring is neither new nor novel, in real world scenarios plenty of difficulties arise in effectively following this approach
In this paper we propose Trident, a coarse grained profiling tool tailored to the High-Energy Physics (HEP) community
To evaluate our Trident tool and to see its usability in real world scenarios we compared the data collected from a known set of benchmarks and a HEP experiment’s sample workload
Summary
The concept of performance counter monitoring is neither new nor novel, in real world scenarios plenty of difficulties arise in effectively following this approach. Even if overcoming all this was possible for a specific system, scaling these to work in a generic way across a large number of systems is challenging due to the vast variety of configurations. From all this it became apparent that the online counter data collection system had to be light weight, secure and intelligent enough to detect the specific counters supported by the architecture. All of this had to be done in a non-obscure way that does not sacrifice configurability to support a large variety of systems
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