AWS Graviton2, Batch and multi-arch docker

Amazon Web Services (AWS) introduced a new 64-bit ARM Neoverse core which they named Graviton2 about a year ago with wide availability in June 2020. Marketing claims they provide up to 40% better price performance over x86-based instances. A footnote clarifies with “20% lower cost and up to 40% higher performance based on internal testing with varying characteristics of compute and memory requirements”. As you know, details can be important....

unconventional magnetic recording

Given the previous posts on magnetic recording, we are now in the position to look at unconventional magnetic recording. In other words, instead of applying a write field of sufficient strength to switch magnetic grains, we look at other ways to switch a magnetic grain. This has become to be known as “energy assist magnetic recording”. In future recording systems this will be required since there is a limit on the applied field one can produce in a small area....

on modeling infection

Compartmental models are often used to simplify the mathematical modeling of infectious disease. The population is split into compartments and it is assumed each individual in a compartment has the same characteristics. While this may appear to be rather crude, such models have shown to accurately model previous infectious outbreaks[1]....

light-weight way to capture results from cloud HPC/HTC

For large HTC (or HPC) computations on the cloud, ‘spot instances’ (AWS-speak), ‘low-priority VM’ (Azure-speak) or ‘preemptible VM instances’ (GoogleCloud-speak) are the low cost options for compute. Of course, the challenge here is that these instances/VM can vanish at anytime. If you’re doing a large HTC task, you want to make sure you save your result (and/or checkpoint) files as soon as they are generated to persistent storage. Otherwise you lost the computation you just paid for....