Last week’s “Ask Managers Anything” question was “How are you making sure that junior staff get access to mentoring when everyone is working from home?” I got several replies back; paraphrasing them for anonymity:
Thanks for your answers! I may try a couple of these myself
This week’s top question is, “How do you start difficult conversations with team members?” My own answer (which echos an article in the roundup list this week) is to try to reduce the need for them by having less-difficult (but still difficult) small conversations early. And when they are necessary, I try to think it through and then start the conversation, with a pretty firm view about what a desired big-picture outcome looks like but with an open and questioning mind about what the underlying issue is and how to address it. I … don’t always succeed, but I’ve gotten noticeably better/less bad at both the early and later conversations over time.
How about you - do you have any advice for our question asker or the community? How do you start difficult conversations with team members? Do you have a go-to phrase that helps or a process? Let me know (just respond to this email) and whether or not I can credit you by name and I’ll include it in the roundup. And feel free to ask (and vote on) questions for next week here.
In other news, some of you may also have noticed some different URLs to the job board amongst other things; I’m slowly moving the newsletter and related information on to its own website, www.researchcomputingteams.org, so it’s less about me. Input as always is welcome (feedback is a gift - PRs doubly so!). If all goes well, I’ll finish the migration next week.
Handling difficult conversations - Rachel Hands, Managing Equitable, Effective, Teams
As above, difficult conversations don’t get easy, but they do get easier. And once you’re a manager, as Hands says,
It’s imperative that you, as a manager, initiate tough conversations when the need arises.
There’s no way through but through, though, so Hands recommends identifying what’s making you uncomfortable about having the conversation so as to defuse it a little bit, and then to focus on the (very specific, observable) issue at hand and that you’d like to start addressing. Then during the conversation, listen a lot, be open to solutions (or even restatements of the problems), and don’t get sidetracked.
None of that makes it easy, but focussing on future outcomes and being aware of what’s making you uncomfortable helps.
Hands points to similarities with the feedback model she advocates, and I’ll just add as above that giving frequent, specific, early feedback can greatly reduce the number of Big Conversations like this that you need to have. Many short slightly difficult conversations » fewer very difficult conversations.
Create space for others - Will Larson
One of the hardest things about a transition to leadership, either on the people-manager or technical-leadership track, is stepping further and further back from directly making contribution and spending more time making room for others, nurturing their contributions, and gathering their input. In this article, Larson describes how that works at the Staff+ Engineer level at large tech companies.
How to Turn around a Disengaged or Underperforming Employee - Lighthouse
Tactical Challenges In Hiring Junior Engineers - Cindy Sridharan
These two articles benefit from being read together. The topics are quite different but they both speak to the need for managers to invest time in new and/or struggling team members.
In research computing we tend to both not reassign or remove employees who aren’t good matches and not invest enough in employees who are struggling. It’s a bad combination, it hurts team morale, it hurts the struggling team members, and it hurts the research we’re trying to support.
The first article talks about what’s necessary in coaching an underperforming team member. It’s a lot of work, for both you and the team member. I think the blog post lays it out well, and if the topic interests you you should read it. The only things I’d add are:
The second article is very upfront about what’s involved in hiring junior team members:
I strongly believe that if a team isn’t willing to invest at least 1–2 years, they shouldn’t be hiring junior engineers.
This is especially true in research computing, Our junior staff tends to be straight out of undergrad or coming from a couple of years in industry; our senior staff tends to be out of Ph.D. programs/postdocs. Not only is there a gap in experience, but there’s huge cultural gap. The process and mindset of doing research will be completely new to them. It’s good that they’re bringing in new mindsets and approaches! We don’t want to quash that. But the cultural difference will have to be bridged to make sure communication works and and expectations are clear.
How to make your data science team faster (and speed up progress) - Gregg Detre, Making Data Mistakes
I’ve got this under manage your own career because the article is really about having tough conversations with your own manager about your team’s (perceived) progress. When talking with your manager, like any other stakeholder, the reported symptom (your team isn’t going fast enough) may be pretty different from the actual underlying problem, so the key is to not get defensive and to not jump to conclusions about what you interpret the problem to be but to dig in and get more information about what the specific cause for concern is so you can address it better. Feedback is data, but data is generally noisy and incomplete; sometimes you need to collect more data before you know what the right next step is.
My Screencasting Workflow - Laurie Barth
Screencasts can be very useful for training materials. Very experienced screen caster Laurie Barth offers her workflow here. Barth records the screencast without audio, then adds annotations, and only when after that does she record an audio trac narrating it. That extra step seems like it would take extra time, but I’ve tried the whole narrating-while-typing thing before and it’s pretty hard and took either extra takes or living with lots of “umms”. I’ll try this next time.
Building digital workforce capacity and skills for data-intensive science - OECD Science, Technology and Industry Policy Papers
This white paper takes a careful look at to what the workforce needs will be to enable data-intensive science in both the public and private sectors. They take a close look at 13 case studies, and it’s worth reading if you’re interested - it’s only about 40 pages. Maybe most crucially, a key take-away of the report is:
There is a need for both digitally skilled researchers [..] and a variety of professional research support staff, including data stewards and research software engineers.
This isn’t surprising but as just the latest calls for professional recognition of the sort of work research computing staff performs, it’s good to see. Also called for are career ladders for such staff within Universities and other research institutions.
Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985–2015) - Hermosilla, Wulder, White, and Coops
A nice example of what people will do as soon as datasets are made available. Canada’s Landsat data over 20 years is open access, and this team cleaned integrated all of the data (from 4 satellites) into a massive number of time series, using change-detection techniques to analyze disturbances. While wildfires were the greatest source of disturbance, areas exposed to anthropogenic disturbances were much more likely to see a distinct second disturbance later on in the time series.
Julia 1.5 Highlights - Jeff Bezanson & Stefan Karpinski
I’m still a little wary of adopting Julia after having my heart (and code) broken quite a few times in the lead-up to 1.0, but Julia’s power in creating really cool and performant DSLs for particular problem areas - look at JuliaStats, DifferentialEquations.jl, JuliaFEM, and others - is hard to deny.
1.5 has some cool features, including sending time-travelling reproducible bug reports using the rr tool which has made an appearance on the newsletter before. It also has more of the same things which has always worried about the state of the product management - a relitigation of how scoping works (!) with a compromise about how it will work one way in the REPL and another way in compiled code (!!) with appropriate warnings.
Still, it’s a cool language and people are building neat things with it, especially in numerically-intensive areas of research computing.
Writing and publishing a Python module in Rust - William Woodruff
One of the things I’ve always liked about Python for research computing is that it lets you prototype things quickly and then, once things are working, swap out slow pieces of code for faster compiled modules. In this article, Woodruff describes the surprisingly simple journey of writing and publishing a Python module written almost completely in Rust. It sounds like there’s still a couple of rough edges around distribution, but it’s remarkably simple, even compared to C- (and especially to C++)-based code. This will be interesting to watch.
Getting Started with Dafny: A Guide - Microsoft Research
Dafny is a research programming language by Microsoft that allows you to add pre- and post-conditions to routines (or even blocks) which are then verified statically upon compilation - essentially making just building your code equivalent to running a large suite of unit tests. The Getting Started Guide is an interactive introductory tutorial for the language.
This sort of incremental “hardening” of code could be extremely useful for research computing (especially for development of code that relies on subtle numerics or algorithms). Computers are way better at the kind of subtle book-keeping that ensures loop invariants are held or assumptions are valid than we are, and anything that pushes such bookkeeping to the computers is of interest to me.
(Relatedly - a theorem proving language has just recently been used to show that a 30-year-old oft-quoted result for Bloom filters is wrong, and proved the final result.)
Drawing good architecture diagrams - Toby W, (UK) National Cyber Security Centre
A nice overview of drawing architecture diagrams. The article makes the point that the diagram is about communicating, and if it doesn’t communicate the key points of the system to the readers, then it’s not succeeding. I like this advice:
Start with a basic high level concept diagram which provides a summary. Then create separate diagrams that use different lenses to zoom into the various parts of your system.
Having multiple diagrams of the same system viewed through different “lenses” seems more likely to be a success than trying to cram everything into one diagram.
An Introduction to ZFS: A Place to Start - Nick Fusco, STH
This is a nice simple introduction to ZFS for those who might be thinking of using it with Linux. It describes how ZFS divides storage unit (disks, VDEV, and pools) and how ZFS’s approach to RAID works within a pool, the advantages (snapshots) and disadvantages (IOPS, fragmentation) of the Copy-on-Write approach and journalling, and why ZFS in particular benefits from SSDs. It also covers, a little bit, some key configuration parameters.
Modernizing the HPC System Software Stack - Allen, Ezell, Peltz, Jacobsen, Roman, Lueninghoener, and Wofford
This paper - by authors at the DOE NNSA facilities that know a thing or two about running large “big iron” HPC system - advocates for drastically updating a traditional HPC stack:
the HPC community has allowed system software designs to stagnate, relying on incremental changes to tried-and-true designs to move between generations of systems.
They argue for more service nodes using modern, horizontally scalable (and thus resilient/available) cluster services, managing extremely bare compute nodes that support something like containers for jobs, more configuration management, better state management (and thus security) and orchestrations, pointing out specific places where the HPC community can learn from what is going on in the broader big-computing world.
I think most or all of these system stack updates would be welcome, but a bigger (and possibly prerequisite) shift will have to be in operations culture; focusing on customer-facing service levels and metrics, which can then lead to “chaos monkey-lite” approaches like Slack’s Disasterpiece Theatre; this sort of focus will, I believe, lead inevitably to more modern system setups in HPC, just as it has elsewhere.
2020 Workshop on Languages and Compilers for Parallel Computing - Submission Deadline 13 Aug, Virtual Conference 14-16 Oct
A long-running (1988!) conference on parallel programming systems.
5th IEEE International Conference on Fog and Edge Computing 2021 - Papers due 3 Jan 2021, event 10-13 May 2021, Melbourne Australia
Readers will know that I’m cautiously optimistic about Fog and Edge computing for data gathering and processing in place for research computing applications. This is one of the big events in the field.
Containers in Production - Online, 10-11 Aug, Free
OpenDev is having a 2 day workshop covering topics like containers and OpenStack, Security and Isolation, Telco and Network Functions, Bare metal and containers, and Acceleration and optimization. Full schedule is here.
High Performance Computing Autumn Academy - Online, 7-18 Sept, Application due 14 Aug, £350-990
Cambridge’s Centre for Scientific Computing is holding it’s autumn HPC course online this year. “The overall aim of this course is to provide course attendees with a strong background in programming techniques suitable for general scientific programming.”
Scientists rename human genes to stop Microsoft Excel from misreading them as dates - this was such a pervasive and recurring problem that scientific nomenclature was changed to avoid the problem.
Using travis, and travis secrets and encryption to deploy via ssh.
A fork of make with tracing and a debugger.
Migration stories are always interesting. Here’s the story of how LinkedIn rewrote its messaging infrastructure while keeping it up and running the whole time.
A nice story on the 60-year history of algorithms for multiplying very large numbers, and the recent proof of an n log n algorithm (but not a lower bound!) involving FFTs.
Termpdf.py, a PDF file viewer for the terminal.
Shournal - ever used script to record a terminal session, or history to reconstruct what you did? This records not only shell history across sessions, but also (using fa-notify) what files were touched (even if they don’t appear explicitly in the command line) for improving reproducibility of processes.
Herbie, a really neat web service for rewriting floating-point expressions to improve accuracy.
Parabol, with free plans for up to two teams, looks like a useful tool for retrospectives.