One of the projects that I've been supporting this year is applying natural language processing techniques to a technical publication dataset to help identify activities that might be related to nuclear proliferation work. As the "data czar" for this project, I investigated multiple sources (e.g., Scopus, Semantic Scholar, OSTI) where we could get a baseline corpus of technical papers we could inspect. In the end, OSTI.gov was the least difficult to obtain and had a lot of interesting info in it. I pulled a large chunk of it, organized it, and did the first pass data engineering to get it into a usable form (turns out there are literally hundreds of ways Sandians identify their laboratories). Jon, Danny, and Zoe did a good bit of analysis on this data, which resulted in this paper at the Institute of Nuclear Materials Management (INMM) annual meeting.
Scientific and technical publications can provide relevant information regarding the technical capabilities of a state, the location of nuclear materials and related research activities within that state, and international partnerships and collaborations. Nuclear proliferation analysts monitor scientific and technical publications using complex word searches defined by fuel cycle experts as part of their collection and analysis of all potentially relevant information. These search strings have been refined over time by fuel cycle experts and other analysts but represent a top-down approach that is inherently defined by the requirement of term presence. In contrast, we are developing a bottom-up approach in which we develop topic models from a small number of expert refereed source documents to search similar topic space, with the hope that we can use this method to identify publications that are relevant to the proliferation detection problems space without necessarily conforming to the expert-derived rule base. We are comparing our results of various topic modeling and clustering techniques to a traditional analyst search strings to determine how well our methods work to find seed documents. We also present how our methods provide added benefit over traditional search by organizing the retrieved documents into topic-oriented clusters. Finally, we present distributions of author institutions to facilitate a broader perspective of the content of interest for analysts.
Recently, I needed to dig up some references to papers I either read or wrote back in the 1990s about programmable network interface cards. Out of curiosity, I did a search for my own dissertation on the web to see if it's floating around somewhere, 17 years after I published it. It didn't surprise me that some of the slide-scraping sites had copies of my defense presentation slides (I made these available on my Ga Tech website). However, I was surprised to find two sites that claimed to have an electronic copy of the actual dissertation since I had never made it available. As it turns out, the Georgia Tech library scanned in the paper version a few years after I graduated (cool!). The other place was some scraper site in China (maybe not so surprising).
The GT Library webpage said they didn't have permission to share the dissertation with people outside GT, so I contacted them and submitted the paperwork to make it world readable. The pdf download was disappointing though- it was 40MB in size (!) and had scanner burn on several of the pages. It occured to me that I could generate a better version, resurrected from my old files. That snowballed into a lot more work than I wanted, but I finally finished it and have added it to this website. Wading through it has given me an opportunity to reflect on what I wrote.
Converting to LaTeX
Resurrecting my dissertation was an absolute chore. At the time, my advisor was curious as to whether modern WYSIWYG editors were solid enough for a dissertation, so he suggested that we buck the time-honored trend of usng LaTeX and have me write it in MS Word 2000. It seemed like a valid, harmless decision when I started, but by the end of the writing it was a constant battle to get the document done before word corrupted it in some unfixable way. To this day, I still have a fear that I'll open a word document and all my section headers will have a mysterious "Char Char Char" phrase prepended to the section title. It was handy to be able to use Power Point and Excel to do my figures and plots, though. Plus, my advisor did periodically use the Track Changes feature to get me comments and corrections. It just would have been nicer to have something in between Word (hard to precisely control) and LaTeX (hard to view while writing).
For the conversion proces, I loaded each chapter into Libre Office and then exported to either text or LaTeX depending on how complicated the text was (the LaTeX output always seemed to spew a lot of extra junk that needed to be filtered out). GT had a standard thesis/dissertation template available that did most of the document boilerplate work for me. The hard part about this process was writing a bunch of one-off awk/grep scripts to correct all the formatting mistakes that happened during export. Importing all the figures was nother problem, but I found the modern version of word let me save my Power Points/plots to pdf, which I could then trim with Linux tools. Done. The last chore was proofreading the text and fixing the bibliography. 17 years is a long time for references to stay valid and many of the product white papers simply disappeared. In the end I think I produced a pretty decent spin of my dissertation that's only 1.6MB in size. I've added a post with the dissertation back on 11/19/2002 when it happened.
Better Material than Expected
I'll admit that when I started reading my dissertation I had low expectations about the content. While I put a lot of work into my research topic, I've always felt like it was a 5% research / 95% development effort. Everyone that starts grad school thinks they'll hit some keen idea that will come up with a new way to do things that will beat quicksort, get around the Nyquist sampling rate limits (compressed sensing kinda did!), or cure cancer. Over time, most people realize that the idea tree was picked clean by the 1960's, and that most of what we've been doing since then is reacting to improvements in technology. Still, there's a lot of snobbery among researchers that if you're not writing a lot of theorems, lemmas, and QEDs in your papers, you're not doing research. My dissertation had zero proofs so I've always felt like I messed up somewhere.
Reading the text again though, I realized I explored a lot of ideas that people hadn't dug into much at the time, and that some of those ideas were things that have only become important to others in the last decade. My thesis was about how you could design a message layer that ran on a programmable NIC and managed all the gritty details about communication so that both host CPUs and peripheral devices could access the network. My word did all the things other people did at the time (low-latency, high-bandwidth messages between hosts, RDMAs to physical and virtual memory, network-interface based multicast!), plus it let you steer data to multimedia cards (video capture/display, FPGA accelerators, and storage cards). In retrospect, this kind of thing became a lot more important 5 years later when people needed a way to route data between GPUs, or more recently when vendors returned to building Smart NICs so people could embed operations in the fabric. While my dissertation had zero impact on any of this, it at least feels good to look back on it and see that I was on the right track.
The main negatives I had about my dissertation was that it was simply too long and filled with details that nobody would care about. After five years of Ph.D. work, I had a chip on my shoulder and wanted to write about every single aspect of what I had done, no matter how boring it was. I understand now that conciseness is the key to good writing, and that giant chunks of text could have been moved to an appendix or dropped entirely. When profs commented about how much text there was, I remember telling them I wanted it there so I'd have it for myself to read later. Well, grad-school-Craig, mid-career-Craig wants you to know he appreciates the sentiment, but he doesn't want to read all of that either. As they say out here in future land, ain't nobody got time for that.
Reading my dissertation reminded me of all the great conversations I had with my advisor, Sudha Yalamanchili, during those years (and in later work visits). Last year Sudha passed away after a long, quiet fight with cancer. GT was not exactly a friendly school, but Sudha always had an optimism to him that made me want to stay longer and try out new ideas. While I made grad school go on longer than it should have, I'm proud of the work I did with this dissertation and am glad that I had Sudha to guide me through the whole process.
Scott's did a lot of work over the last year collecting stats about how Lunasa (FAODEL's memory management system) can be used to improve performance in different types of communication scenarios. It turns out there are a lot of dirty secrets hidden in NIC device drivers, like simply de-registering memory can be pretty significant. Scott put a paper together making the case for using explicit memory handles when dealing with network data, instead of letting the communication layer take care of everything. There's a good Kokkos use case in the paper that gives an idea about how HPC is evolving, and he has numbers for both Mutrino (Cray/Gemini) and Stria (ARM/InfiniBand).
Remote Direct Memory Access (RDMA) is an increasingly important technology in high-performance computing (HPC). RDMA provides low-latency, high-bandwidth data transfer between compute nodes. Additionally, it does not require explicit synchronization with the destination processor. Eliminating unnecessary synchronization can significantly improve the communication performance of large-scale scientific codes. A long-standing challenge presented by RDMA communication is mitigating the cost of registering memory with the network interface controller (NIC). Reusing memory once it is registered has been shown to significantly reduce the cost of RDMA communication. However, existing approaches for reusing memory rely on implicit memory semantics. In this paper, we introduce an approach that makes memory reuse semantics explicit by exposing a separate allocator for registered memory. The data and analysis in this paper yield the following contributions: (i) managing registered memory explicitly enables efficient reuse of registered memory; (ii) registering large memory regions to amortize the registration cost over multiple user requests can significantly reduce cost of acquiring new registered memory; and (iii) reducing the cost of acquiring registered memory can significantly improve the performance of RDMA communication. Reusing registered memory is key to high-performance RDMA communication. By making reuse semantics explicit, our approach has the potential to improve RDMA performance by making it significantly easier for programmers to efficiently reuse registered memory.
Patrick Widener put together a paper on using FAODEL to deal with data center storage diversity for ISC 2019. This paper gets into some of the ideas we've had about how to use data services to route to different storage targets, and highlights some of the HDF5/LevelDB interfacing that Patrick's done.
Composition of computational science applications into both ad hoc pipelines for analysis of collected or generated data and into well-defined and repeatable workflows is becoming increasingly popular. Meanwhile, dedicated high performance computing storage environments are rapidly becoming more diverse, with both significant amounts of non-volatile memory storage and mature parallel file systems available. At the same time, computational science codes are being coupled to data analysis tools which are not filesystem-oriented. In this paper, we describe how the FAODEL data management service can expose different available data storage options and mediate among them in both application- and FAODEL-directed ways. These capabilities allow applications to exploit their knowledge of the different types of data they may exchange during a workflow execution, and also provide FAODEL with mechanisms to proactively tune data storage behavior when appropriate. We describe the implementation of these capabilities in FAODEL and how they are used by applications, and present preliminary performance results demonstrating the potential benefits of our approach.
A few years ago we stood up the Carnac cluster so our Emulytics users would have a place to do large-scale virtual network experiments. Unlike our HPC clusters which use InfiniBand or OmniPath, Carnac was built around a 100GigE network fabric based on Mellanox NICs and a large Arista switch. While the 100GigE network was much more expensive than InfiniBand, it provides a more natural conduit for Emulytics experiments. Given that we knew portions of Carnac would be idle between jobs, we wondered if we could borrow some of the nodes from time to time and run MPI jobs efficiently.
Our initial tests using MPI over TCP were abysmal as expected. TCP's latencies were pretty bad and we found you really had to open up a lot of simultaneous connections to get anywhere near the available bandwidth. About a decade ago there was a lot of interest in using RDMA over Converged Ethernet (RoCE) to improve this performance. RoCE is interesting because it tries to get Ethernet hardware to behave more like HPC hardware. On the host side, RoCE provides an InfiniBand API that HPC comm libs are used to using. RoCE NIC vendors have written OS bypass libs that allow userspace applications to directly talk with message queues on the NIC. In the fabric, RoCE messages are marked in special Ethernet frames so RoCE-aware switches can handle them more efficiently (ie, use link-level flow control to avoid drops).
RoCE has been our there for a while, but you don't see many people using it much these days. Joe Kenny set about trying to configure our switches and NIC to use it. At first it seemed like it was working, but in longer experiments he saw enough lock ups to indicate that there were incompatibilities in the hardware. He pleaded for help in an OFED talk but found no solutions. He borrowed a switch from Mellanox but got nowhere. Just as we were about to call it dead, he stumbled into some settings that fixed things. Things also worked fine back on our core Arista switch. It's frustrating that we don't have a good explanation for why things did/didn't work, but I pushed Joe to document what we went through in a SAND Report. It's SAND2019-13444.
Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) has the potential to provide performance that rivals traditional high performance fabrics. If this potential proves out, significant impacts on system procurement decisions could follow. This work provides a series of small scale performance results which are used to compare and contrast the performance of RoCE-enabled Ethernet with TCP-based Ethernet and an HPC network. Additionally, a discussion of the maturity of RoCE firmware/software stacks and documentation is provided along with useful approaches for probing performance. A detailed description of two experimental setups known to have good RoCE performance is given, including step-by-step configuration and the exact hardware and software revisions employed. At small scales, RoCE is found to have significant performance advantages over "out-of-the-box" TCP protocols and is competitive with state-of-the-art high performance networks. Further examination of RoCE using a wider array of benchmarks and at greater scale is warranted.