Running HPC workloads across multiple architectures with Red Hat Enterprise Linux

In this article, I want to provide some background details about our recently developed demonstration video – “Running Game of Life across multiple architectures with Red Hat Enterprise Linux“.

This video shows the Game of Life running in a heterogeneous environment using three 64-bit hardware architectures: aarch64 (ARM v8-A), ppc64le (IBM Power little endian) and x86_64 (Intel Xeon). If you are not familiar with the rules of this cellular automaton, they are worth checking out via the reference above.

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The need for speed and the kernel datapath – recent improvements in UDP packets processing

Networking hardware is becoming crazily fast, 10Gbs NICs are entry-level for server h/w, 100Gbs cards are increasingly popular and 200Gbs are already surfacing. While the Linux kernel is striving to cope with such speeds with large packets and all kind of aggregation, ISPs are requesting much more demanding workload with NFV and line rate packet processing even for 64 bytes packets.

Is everything lost and are we all doomed to rely on some kernel bypass solution? Possibly, but let’s first inspect what is really the current status for packet processing in the kernel data path, with a perspective look at the relevant history and the recent improvements.

We will focus on UDP packets reception: UDP flood is a common tool to stress the networking stack allowing arbitrary small packets and defeating packet aggregation (GRO), in place for other protocols.

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Scaling Sync

Introduction

One of the biggest challenges for developers to build mobile applications is data synchronization. It’s the foundation for many different types of mobile applications, but it’s very complicated and very hard to implement.

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Fighting Service Latency in Microservices with Kubernetes

CPU and network speed have increased significantly in the last decade, as well as memory and disk sizes. But still one of the possible side effects of moving from a monolithic architecture to Microservices is the increase in the service latency. Here are few quick ideas on how to fight it using Kubernetes.

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Benchmarking nftables

Since I’ve learned about nftables, I heard numerous times that it would provide better performance than its designated predecessor, iptables. Yet, I have never seen actual figures of performance comparisons between the two and so I decided to do a little side-by-side comparison.

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Java inside docker: What you must know to not FAIL

Many developers are (or should be) aware that Java processes running inside Linux containers (docker, rkt, runC, lxcfs, etc) don’t behave as expected when we let the JVM ergonomics set the default values for the garbage collector, heap size, and runtime compiler. When we execute a Java application without any tuning parameter like “java -jar mypplication-fat.jar”, the JVM will adjust by itself several parameters to have the best performance in the execution environment.

This blog post takes a straightforward approach to show developers what they should know when packaging their Java applications inside Linux containers.

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