How to Prepare a Load Test for the Purpose of Performance Tuning
Having worked on optimizing multiple environments and applications over the years, the main challenge our users face is how to properly prepare a load test for the purpose of tuning their applications. In order to make quality recommendations for system settings that... Read More
Concertio to Present Its Automatic Kubernetes Optimization Solution at KubeCon 2019
Concertio, the leading provider of AI-powered performance optimization toolkit for turning general-purpose systems into high-performant, tailor-made systems, will be presenting its automatic Kubernetes optimization solution at the KubeCon conference, November 19-21,... Read More
Concertio Recognized by 451 Research as a ‘451 Firestarter’
Concertio recognized by leading analyst firm for innovation and vision in the technology industry New York City, March 28, 2019 – Concertio today announced it has received a 451 Firestarter award from leading technology research and advisory firm 451 Research,... Read More
Automatically Accelerating TensorFlow Workloads on Intel CPUs
See how automated optimization increases ResNet50 performance by 1.77x One of the first thoughts that comes to mind about deep learning and AI is the hope that someday we might be able to develop cognition in computers, that our creations would be able to think on... Read More
Automating compiler flag mining: to -O3 or not to -O3?
Selecting the right compiler flags for Coremark on Marvell’s ThunderX2 Processor One of the first things you learn in Compilers 101 is that compiler optimizations can do wonders to speed up binaries. Although it has been a very active field of research for decades,... Read More
Dealing With Performance Variability When Tuning Systems
How to determine which configuration performs better It is said that the definition of insanity is doing the same thing over and over and expecting a different result. But as any performance engineer would tell you, running the same workload again and again does... Read More