Announcements and Media Coverage

News Announcements

Concertio Launches a Free Community Version of its Optimizer Studio Static Optimization Product

Concertio, the leading provider of AI-powered performance optimization software, announced today the availability of a free community version of its Optimizer Studio static tuning product.

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Concertio Raises $4.2M Seed Round to Scale Operations

Concertio is proud to announce the closing of our $4.2M Seed Round. The funds will be used to scale operations of our AIOps Optimizer platform and to further Concertio’s technology lead in dynamic, continuous, and static optimization. Please find the full press release below…
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Concertio Offers Free Access to its Optimization Technology to Support the Fight Against COVID-19

New York, New York, March 23, 2020 – Concertio, the leading provider of AI-powered performance optimization software, announced today that it will grant free access to Concertio’s optimization tools throughout 2020 to accredited research institutions and private…
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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,…
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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,…
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Media Coverage

Automated Optimization Boosts ResNet50 Performance by 1.77x

From supercomputers to cell phones, every system and software device in our digital panoply has a growing number of settings that, if not optimized, constrain performance, wasting precious cycles and watts. In the fast-growing field of AI, optimized systems yield faster training times and require less infrastructure. But the tuning process can be tedious and requires specialized skills. Startup Concertio, creator of performance optimization toolkit Optimizer Studio, is asking the question, “can we relieve data scientists from the need to understand their specific underlying infrastructure and from the need to optimize the performance of their models?”
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This NYC Startup Uses AI to Optimize Your Servers

A company’s technology stack functions much like an orchestra; many parts working together in harmony, but if one instrument is too slow or out of tune, the entire company runs less effectively. Concertio, previously known as DatArcs, serves as an AI-driven maestro that optimizes and orchestrates your software and hardware deployments. When deploying and maintaining your server, there are a myriad of options that can be configured and optimizing this architecture across your technology stack ensures that the show runs smoother.
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Podcast: Machine Learning for Auto-Tuning HPC Systems

While the role of performance engineer will not disappear anytime soon, machine learning is making tuning systems—everything from CPUs to application specific parameters—less of a burden. Despite the highly custom nature of systems and applications, reinforcement learning is allowing new leaps in time-saving tuning as software learns what works best for user applications and architectures, freeing up performance engineers to focus on the finer points of system behavior.
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Startup Concertio offers AI-optimized server configuration

AI-Powered / Machine Learning Linux Performance Tuning Is Now A Thing

A year and a half ago I wrote about a start-up working on dynamically-tuned, self-optimizing Linux servers. That company is now known as Concertio and they just launched their “AI powered” toolkit for IT administrators and performance engineers to optimize their server performance.
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Start-up Aims AI at Automated Tuning of Complex Systems

There was a simpler time when system tuning consisted of adjusting a relatively few number of knobs, a manual and not overly demanding task that brought out the best in systems performance. But now, as we move toward accelerated enterprise systems networked from data center to public cloud to mobile and sensored devices at the edge, simplicity in systems tuning is long gone.
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