Measuring directly from cpu hardware performance counters

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CPUs embed a performance monitoring unit (PMU) which we can use to collect data on cache usage (hit/miss) branch predictions, instructions, cycles, etc. those counters can be accessed in Java thanks to the overseer library.

Logiciels

Measuring directly from CPU :
Hardware performance
counters
Jean-Philippe BEMPEL
Performance Architect
@jpbempel
http://jpbempel.blogspot.com

●Measuring time
○ System.CurrentTimeMillis()
○ System.nanoTime()
●Timestamps in logs
●Profiling
Measuring

● System.currentTimeMillis()
1ms -> 10-15ms depending on the OS
● System.nanoTime()
500ns -> 1us
Précision

● CPUs embeds PMU (Processing Monitoring Unit)
● Hardware counters on CPU behavior:
○ Cycles
○ Instructions
○ Caches
○ Memory accesses
● predefined or customizable
Compteurs de performance

●available on most linux distros
●Allow to access perf counters but allow other things
(profiling, OS counters, …)
●list perf counters: perf list
●Profiler with default counters
perf stat java…
●Customize a perf counter
perf stat -e LLC-load-misses java ...
Perf

● Overseer library:
○ http://www.peternier.com/projects/overseer/overseer.php
● libpfm4:
○ http://perfmon2.sourceforge.net/
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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

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Measuring directly from cpu hardware performance counters

1. Measuring directly from CPU : Hardware performance counters Jean-Philippe BEMPEL Performance Architect @jpbempel http://jpbempel.blogspot.com

2. ●Measuring time ○ System.CurrentTimeMillis() ○ System.nanoTime() ●Timestamps in logs ●Profiling Measuring

3. ● System.currentTimeMillis() 1ms -> 10-15ms depending on the OS ● System.nanoTime() 500ns -> 1us Précision

4. ● CPUs embeds PMU (Processing Monitoring Unit) ● Hardware counters on CPU behavior: ○ Cycles ○ Instructions ○ Caches ○ Memory accesses ● predefined or customizable Compteurs de performance

5. Hiérarchie mémoire

6. échelle de latence

7. ●available on most linux distros ●Allow to access perf counters but allow other things (profiling, OS counters, …) ●list perf counters: perf list ●Profiler with default counters perf stat java… ●Customize a perf counter perf stat -e LLC-load-misses java ... Perf

8. DEMO Perf

9. ●java library (JNI - linux only) ●Allow to use all available counters ●Based on libpfm4 ○Encodes counters based on CPU ●Profile precisely part of the code of applications Overseer

10. DEMO Overseer

11. DEMO Memory layout: LinkedList

12. DEMO Memory layout: ArrayList

13. Pipeline

14. ● Overseer library: ○ http://www.peternier.com/projects/overseer/overseer.php ● libpfm4: ○ http://perfmon2.sourceforge.net/ Liens

15. Thanks for your attention. Questions?