In-Memory Computing Planet® Blogs and Events

Introduction

Cloud computing is on the rise for a couple of reasons: it is flexible, relatively cheap compared to supporting in-house infrastructure, and it allows excellent automation of resource allocation, thus cutting costs even more.

There is a trend in industry which says that modern applications need to be built on top of one or more special-purpose databases. That every application benefits from using the best-of-breed technology for each requirement. And that the plethora of special-purpose options available from certain cloud providers is reasonable to manage. That’s all BUNK. The reality is that navigating the choices, figuring out how to use them effectively, and dealing with the ETL and inevitable data sprawl, is so difficult that the pain far outweighs any technical advantage you might get. In the vast majority of use cases, a single …

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Back when Jeff Bezos filled orders in his garage and drove packages to the post office himself, crunching the numbers on costs, tracking inventory, and forecasting future demand was relatively simple. Fast-forward 25 years, Amazon's retail business has more than 175 fulfillment centers (FC) worldwide with over 250,000 full-time associates shipping millions of items per day.

Amazon's worldwide financial operations team has the incredible task of tracking all of that data (think petabytes). At Amazon's scale, a miscalculated metric, like cost per unit, or delayed data can have a huge impact (think millions of dollars). The team is constantly looking for ways to get more accurate data, faster.

That's why, in 2019, they had an idea: Build a data lake that can support one of the largest logistics networks on the planet…

STELLA: report from the SNAFU-catchers workshop on coping with complexity, Woods 2017, Coping with Complexity workshop “Coping with complexity” is about as good a three-word summary of the systems and software challenges facing us over the next decade as I can imagine. Today’s choice is a report from a 2017 workshop convened with that title, … Continue reading STELLA: report from the SNAFU-catchers workshop on coping with complexity

“The US is the undisputed leader in AI development, the Index shows. The western superpower scored almost twice as highly as second-placed China, thanks to the quality of its research, talent and private funding. America was ahead on the majority of key metrics – and by a significant margin. However, on current growth experts predict […]

Updated: For changes in Linux kernel 4.20 and beyond (5.x) Intel Optane SSDs are ultra-fast and we wanted to share a few tips about Linux to help you get the most out of one of the world’s fastest SSDs. Optane is an SSD that can achieve sub-10 microsecond response time of 4 KiB I/O and ...continue reading Tuning the performance of Intel Optane SSDs on Linux Operating Systems

The post Tuning the performance of Intel Optane SSDs on Linux Operating Systems appeared first on IT Peer Network.

Synthesizing data structure transformations from input-output examples, Feser et al., PLDI’15 The Programmatically Interpretable Reinforcement Learning paper that we looked at last time out contained this passing comment coupled with a link to today’s paper choice: It is known from prior work that such [functional] languages offer natural advantages in program synthesis. That certainly caught … Continue reading Synthesizing data structure transformations from input-output examples

Introduction

Cloud computing is on the rise for a couple of reasons: it is flexible, relatively cheap compared to supporting in-house infrastructure, and it allows excellent automation of resource allocation, thus cutting costs even more.

Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a controller learned through reinforcement learning (RL) is one of the key challenges for real-world deployments of RL that we looked at earlier this week. It’s also an essential requirement for agents in human-machine collaborations (i.e, all deployments at some … Continue reading Programmatically interpretable reinforcement learning

Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML’19 Last week we looked at some of the challenges inherent in automation and in building systems where humans and software agents collaborate. When we start talking about agents, policies, and modelling the environment, my thoughts naturally turn to reinforcement learning (RL). Today’s paper choice sets out … Continue reading Challenges of real-world reinforcement learning

Ten challenges for making automation a ‘team player’ in joint human-agent activity, Klein et al., IEEE Computer Nov/Dec 2004 With thanks to Thomas Depierre for the paper suggestion. Last time out we looked at some of the difficulties inherit in automating control systems. However much we automate, we’re always ultimately dealing with some kind of … Continue reading Ten challenges for making automation a ‘team player’ in joint human-agent activity

As interest in MemSQL increases, we get many questions about how MemSQL works, how to get started with it, and more. Performance reviews website G2.com has a MemSQL Q&A section where potential customers can ask questions. Here are some of the questions we hear most often – with answers – lightly edited, for context and clarity. Q. What is the advantage of MemSQL over other distributed databases? A. Compared to relational databases – those which support SQL – we believe that MemSQL is the fastest, most efficient SQL database. MemSQL features full, linear scalability, unlike competitors. We also handle both …

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