Improvements of the ALICE HLT data transport framework for LHC Run 2

TL;DR

This paper details enhancements to the ALICE HLT data transport framework for LHC Run 2, focusing on performance optimization, asynchronous processing, and increased data handling capabilities to support higher event rates.

Contribution

It introduces new optimizations, including asynchronous transport with Zero-MQ and resilient asynchronous components, to improve data rates and robustness in the ALICE HLT framework during Run 2.

Findings

Optimized data transport for higher event rates

Implemented asynchronous processing for resilience

Enhanced configuration and startup procedures

Abstract

The ALICE HLT uses a data transport framework based on the publisher-subscriber message principle, which transparently handles the communication between processing components over the network and between processing components on the same node via shared memory with a zero copy approach. We present an analysis of the performance in terms of maximum achievable data rates and event rates as well as processing capabilities during Run 1 and Run 2. Based on this analysis, we present new optimizations we have developed for ALICE in Run 2. These include support for asynchronous transport via Zero-MQ which enables loops in the reconstruction chain graph and which is used to ship QA histograms to DQM. We have added asynchronous processing capabilities in order to support long-running tasks besides the event-synchronous reconstruction tasks in normal HLT operation. These asynchronous components run in an isolated process such that the HLT as a whole is resilient even to fatal errors in these asynchronous components. In this way, we can ensure that new developments cannot break data taking. On top of that, we have tuned the processing chain to cope with the higher event and data rates expected from the new TPC readout electronics (RCU2) and we have improved the configuration procedure and the startup time in order to increase the time where ALICE can take physics data. We analyze the maximum achievable data processing rates taking into account processing capabilities of CPUs and GPUs, buffer sizes, network bandwidth, the incoming links from the detectors, and the outgoing links to data acquisition.