Last month, Steven J. Vaughan-Nichols of ZDNet alerted us that Linux 4.0 will provide support for “no-reboot patching.” The gist: When a security patch or other critical OS update comes out, you can apply it without rebooting.
While rebootless patching is convenient for everyone, it’s a game changer for some applications. For example, web and cloud hosting services normally require customers to experience some downtime while the OS infrastructure is upgraded; with rebootless patching, upgrades happen seamlessly. Or, imagine upgrades to systems hosting in-memory databases: Right now, you have to checkpoint the DB to stable storage, stop the system, upgrade it, restart it, read the data from stable storage, and restart service. Just the checkpointing and re-reading from disk could take tens of minutes. With rebootless patching, this disruption is avoided; cf. Facebook’s usage of a modified memcached that supports preserving state across updates.
I’m particularly excited by this announcement because I’ve been working on the general problem of updating running software, which I call dynamic software updating (DSU), for nearly 15 years. In this post, co-authored with my PhD student Luís Pina, I take a closer look at the challenge that DSU presents, showing that what Linux will support is still quite far from what we might hope for, but that ideas from the research community promise to get us closer to the ideal, both for operating systems and hopefully for many other applications as well.
During my tenure as a student and professor, I have been to many talks offering career advice to graduate students. Most of these talks focus on careers in research universities and industrial research labs, and leave out discussion of institutions, such as liberal arts colleges, that are primarily concerned with undergraduate education. This is unfortunate because many liberal arts colleges are highly selective institutions that offer exciting careers that mix research and teaching, albeit in a different way than careers in research universities.
One way to reduce the information deficit about liberal arts colleges is to report on the experiences of those who work at one. This is what we do in the present post. Specifically, I interview Steve Freund, who is a professor of computer science at Williams College, ranked by US News as the top liberal arts college in America. Steve is a highly successful PL researcher, known for his significant contributions to the analysis of concurrent programs. As a result, he is in a great position to give PL Enthusiast readers a view into what it’s like to be a teacher and researcher at a liberal arts institution. Continue reading
The Summit on Advances in Programming Languages (SNAPL) is a new kind of PL conference, focused on big-picture questions rather than concrete technical results. The conference will be held for the first time in Asilomar, CA, from May 3 to 6, 2015.
The submission deadline is January 9, 2015 — if you have a long-term vision about where the field of PL should go, you ought to submit a paper.
Here we post an interview with Shriram Krishnamurthi, who is a professor at Brown University and one of the organizers of the conference.
I recently had the pleasure of co-organizing a Dagstuhl Seminar on the synergy between ideas, methods, and research in programming languages and cryptography.
Dagstuhl Seminar on the Synergy between Programming Languages and Cryptography
This post and the next will summarize some interesting discussions from the seminar. In this post, I will look at how programming languages often interface with cryptography, surveying the research of the seminar participants. In my next post, I’ll dig a little deeper into one topic in particular, which is how formal reasoning in PL and Crypto compare and contrast, and how ideas from one area might be relevant to the other.
Ultimately, I came away convinced that the combination of PL and Crypto has much to offer to the problem of building secure systems.
A few weeks ago, I posted about an analysis of collaboration in the POPL community. In that post, I promised similar analyses for a few other conference-defined communities as well. Well, here they are. In this post, I will report on an analysis of community structure in two other premier SIGPLAN conferences: PLDI and OOPSLA.
The methodology for the analysis was similar to that in my earlier post on POPL. The questions I asked were:
- Who works with whom in the community defined by a conference X?
- Are there prominent clusters of researchers who frequently publish papers with each other?
- Which papers/researchers are at the center of the community (that is, who are the Kevin Bacons of community X)?
To answer these questions, I used data from the DBLP database to construct, for each conference, an overlap graph: a graph where nodes represent papers with more than 1 author published in the period 2005-2014, and edges connect pairs of papers that have at least one author in common. For each graph, I generated the set of connected components (which correspond to disjoint subcommunities) and ran some further analyses on the largest component. Continue reading
I was recently in Princeton for the program committee meeting of the POPL conference. It was a lot of fun. David Walker, the program chair, offered excellent leadership, and I am excited about the program that we ended up selecting. I look forward to seeing many of you at the conference (Mumbai, January 2015).
POPL is a broad conference, and you really feel this when you attend its PC meeting. You inevitably discuss papers with fellow PC members whose backgrounds are very different from your own. Of the papers discussed, there are many that use techniques about which you only have rudimentary knowledge.
One thing I kept wondering at the meeting was: is POPL really one research community? Or is it really a union of disjoint sets of researchers who work on different themes within POPL, for example types or denotational semantics or abstract interpretation? Perhaps researchers in these sub-communities don’t really work with each other, even if they share a vision of reliable software and productive programming.
The question was bugging me enough that I decided to try to answer it through an analysis of actual data. The results I found were intriguing. The takeaway seems to be that POPL is indeed one family, but not a particularly close one. Continue reading
In the computing stack, PL sits between algorithms and systems. Without algorithms to implement or computer systems to run them on, there would be no need for programming languages. However, the research communities that study algorithms, PL, and systems don’t really have much of an overlap. This is perhaps unavoidable: computer science is now a mature field, and researchers in mature fields tend to pursue specialized and technical research questions.
At the same time, it seems important that the approaches — assumptions and methods — of different subfields of computing be compatible to some extent. At the end of the day, computer science aims to produce solutions for the whole computing stack. An “impedence mismatch” between its subfields compromises our ability to come up with such end-to-end solutions.
This suggests that the comparative study of assumptions, techniques and cultures of different CS fields (a sort of “comp lit” for computer science) is potentially valuable.
Personally, I have always been intrigued by the relationship between the fields of programming languages and algorithms. In this post, I discuss similarities and differences between these two areas, and argue that their synthesis could be interesting for both research and teaching.