timeu

My father got 3 stents inserted during an angiogram. The reason was most likey that he was suffering from sleep apnea (his breathing stopped around 60 times an hour and his o2 saturation dropped to 50/60) without knowing it. In his case it was central sleep apnea but after he got a CPAP machine his o2 saturation went back up to > 90%. One easy way to check this is to wear a pusle oximater overnight and check the o2 saturation.

As an HPC sysadmin for 3 research institutes (mostly life sciences & biology) I can't see how cloud HPC system could be any cheaper than an on-prem HPC system especially if I look at the resource efficiency (how much resources were requested vs how much were actually useed) of our users SLURM jobs. Often the users request 100s of GB but only use a fraction of it. In our on-prem HPC system this might decrease utilization (which is not great) but in the case of the cloud this would result in increased computing costs (because bigger VM flavor) which would be probably worse (CapEx vs OpEx) Of course you could argue that the users should do and know better and properly size/measure their resource requirements however most of our users have lab background and are new to computational biology so estimating or even knowing what all the knobs (cores, mem per core, total memory, etc) of the job specification means is hard for them. We try to educate by providing trainings and job efficency reporting however the researchters/users have little incentive to optimize the job requests and are more interested in quick results and turnover which is also understandable (the on-prem HPC system is already payed for). Maybe the cost transparancy of the cloud would force them or rather their group leaders/institute heads to put a focus on this but until you move to the cloud you won't know.

Additionally the typical workloads that run on our HPC system are often some badly maintained bioinformatics software or R/perl/pythong throwaway scripts and often enough a typo in the script causes the entire pipeline to fail after days of running on the HPC system and needs to be restarted (maybe even multiple times). Again on the on-prem system you have wasted electricity (bad enough) but in the cloud you have to pay the computing costs of the failed runs. Again cost transparency might force to fix this but the users are not software engineers.

One thing that the cloud is really good at, is elasticity and access to new hardware. We have seen for example a shift of workloads from pure CPUs to GPUs. A new CryoEM microscope was installed where the downstream analysis is relying heavily on GPUs, more and more resaerch groups run Alpafold predictions and also NGS analysis is now using GPUs. We have around 100 GPUs and average utlizations has increased to 80-90% and the users are complaining about long waiting/queueing times for their GPU jobs. For this bursting to the cloud would be nice, however GPUs are prohibitively expensive in the cloud unfortunately and the above mentioned caveats regarding job resource efficiencies still apply.

One thing that will hurt on-prem HPC systems tough are the increased electricity prices. We are now taking measures to actively save energy (i.e. by powering down idle nodes and powering them up again when jobs are scheduled). As far as I can tell the big cloud providers (AWS, etc) haven't increased the prices yet either because they cover elecriticity cost increase with their profit margins or they are not affected as much because they have better deals with elecricity providers.

Funny, when I was young my father who was a geophyisicst (now retired) took me to seismological surveys, where we hit with a hammer on a plate to generate shock waves and measure velocity (mostly for building tunnels through montains). After a hard day of physical work, my father was drawing these lines at specific points of the recorded dataset (sorry I lack the proper terms and vocabulary because it was like 20 years ago) and there are some patterns you can use to identifiy those spots but most of it was based on his intuition and years of experience. Sometimes he let me do the interpretation and all I had was the those patterns, but many times he had to correct it because of what you describe (experience, terrain, environment, etc)

We should also not forget that some people got filthy rich with the whole GWOT.

Yeah, I can feel your pain. We deployed a fully virtualized HPC cluster on top of OpenStack and there was a fair amount of testing and playing with knobs (SRIOV, Jumbo frames, CPU pinning, etc) to get close to bare metal performance on a 100 GBit/s SDN fabric.

Sorry but your second point does not correct. I have relatives in Iran (all my female relatives have finished university in Iran and work). Last time I visted my cousin (this is alreay some years ago) he showed me around the company he works for (IT company) and half of the software engineers there were women.

We are working on a project where we plan to manage our new HPC system with OpenStack. This will replace 3 legacy HPC systems that were manged with propriatory management systems. Due to shifting requirements by our customers (scientist) we decided to move to a cloud framework where probably still the majority of resources is dedidcated to a batch scheduling system but it would allow us to also provide more cloud like services (jupyerhub, rstudio, databases, etc) It's quite an ambitious project and we (4 engineers) basically spent the last year understanding the in and outs of OpenStack. We also went full in with integrating all kinds of datacenter components into OS (NetApp, SDN, DNS, etc)

Some lessons learned so far:

  - OpenStack is very complex     
  - It's less of a product and more a framework and you need a dedicated engineering team with cross cutting skillset     
  - You definately need a dev/staging environment to test upgrades and customizations  
  - Some of the reference implementations of OS servies (SDN) are fine for small deployments but if can replace them with dedicated hardware/appliances you should do that.

I had exactly the same issue. But in my case I setup a device password that probably was in one of the PW leaks. I was listening to spotify on my phone and suddenly it switched to a different device unknown to me and played mostly latin songs. When I changed the device pw it stopped

same experience with the difference that I do carb cycle. on workout days I eat high protein,carb and low fat and on workfree days I eat high protein, more fat and few carbs.

workout without breakfest does work, although if I go to very heavy weights, I tend to take a bit of BCAA before the workout.