Results for “Serial Learning” — Page 3

NASA NTRS2015

Johnson Space Center's Free Range Bicycle Program.- Fall 2015 Intern Report

NASA's Johnson Space Center is a big place, encompassing 1,620 acres and more than a hundred buildings. Furthermore, there are reportedly 15 thousand employees, all of which have somewhere to be. To facilitate the movement of all these people JSC has historically relied on human power. Pedaling their way towards deep space, bicycles have been the go to method. Currently there are about 200 Free Range Bicycles at JSC. Free Range Bicycles belong to nobody, except NASA, and are available for anybody to use. They are not to be locked or hidden (although frequently are) and the intention is that there will always be a bike to hop on to get where you're going (although it may not be the bike you rode in on). Although not without its own shortcomings, the Free Range Bicycle Program has continued to provide low cost, simple transportation for NASA's JSC. In addition to the approximately 200 Free Range Bicycles, various larger divisions (like engineering) will often buy a few dozen bikes for their team members to use or individuals will bring their own personal bike to either commute or use on site. When these bicycles fall into disrepair or are abandoned (from retirees etc) they become a problem at JSC. They are an eye sore, create a safety hazard and make it harder to find a working bike in a time of need. The Free Range Program hopes to address this first problem by "tagging out" abandoned or out of service bicycles. A bright orange "DO NOT OPERATE" tag is placed on the bike and given a serial number for tracking purposes. See picture to the right. If the bike has an active owner with intentions to repair the bike the bottom of the tag has instructions for how to claim the abandoned bicycle. After being tagged the owner of the bicycle has 30 days to claim the bicycle and either haul it off site or get it repaired (and labeled) in accordance with Johnson's Bicycle Policy. If the abandoned bicycle is not claimed within 30 days it becomes the property of the Government. The bicycle is then (in short) repaired, labeled, documented and converted to a free range bicycle. Bikes beyond repair are cannibalized of useable parts and then scrapped. That was nearly the first thing I did when arriving at work. After getting settled in and coordinating the purchase of 50 new bicycles (elaboration below); I started combing the Center. Bikes are hidden and tucked away in the oddest of places and it was my priority to root all of them out. I tagged 70 bikes on the center, setting a record. I tagged so many bikes I ran out tags and had to make more. Long ago it was discovered that the same harsh elements that wreak havoc on the bikes, destroy the tags before the 30 day timeframe. The tags are labeled with instructions on how to claim the bike and numbered, then these paper labels are laminated with packing tape. It sounds like a simple process but nothing fits right, everything has to be trimmed, put on straight and is generally a pain in the neck. So I made an assembly process and knocked out a few hundred to save having to do it again for a while. I walked the center and tagged bikes at every building hiding in nearly every nook and cranny. Thus, in conclusion, I've done many things here at JSC on my first term and had a blast doing it. I've learned a lot from my multi-faceted roles and continual challenges. In short I've done my best to get folks at JSC on bikes and keep folks at JSC on bikes and tended a flock of free range bicycles. Everything from turning the rusty bolts and oiling chains to coordinating a $20,000 deal on 50 new free range bicycles. I am proud of the bicycle shop I have built and am infinitely grateful to the people who lead, help, guide and support me. I have fixed a great number of bicycles and cleaned the center of unsightly and unsafe piles of bicycles. I am immensely thankful for this opportunity to learn and serve and appreciate the privilege of coming back for a second term. A second term, in which I will continue to develop this awesome program.

Lee-Stockton, Willem↗

DOE OSTI2024

Streaming Matching and Edge Cover in Practice

Graph algorithms with polynomial space and time requirements often become infeasible for massive graphs with billions of edges or more. State-of-the-art approaches therefore employ approximate serial, parallel, and distributed algorithms to tackle these challenges. However, such approaches require storing the entire graph in memory and thus need access to costly computing resources such as clusters and supercomputers. In this paper, we present practical streaming approaches for solving massive graph problems using limited memory for two prototypical graph problems: maximum weighted matching and minimum weighted edge cover. For matching, we conduct a thorough computational study on two of the semi-streaming algorithms including a recent breakthrough result that achieves a $1/(2+\varepsilon)$-approximation of the weight while using $O( n \log W /\epsilon)$ memory (here $n$ is the number of vertices and $W$ is the maximum edge weight), designed by Paz and Schwartzman [SODA, 2017]. Empirically, we show that the semi-streaming algorithms produce matchings whose weight is close to the best $1/2$-approximate offline algorithm while requiring less time and an order-of-magnitude less memory. For minimum weighted edge cover, we develop three novel semi-streaming algorithms. Two of these algorithms require a single pass through the input graph, require $O(n \log n)$ memory, and provide a 2-approximation guarantee on the objective. We also leverage a relationship between approximate maximum weighted matching and approximate minimum weighted edge cover to develop a two-pass $3/2+\epsilon$-approximate algorithm with the memory requirement of Paz and Schwartzman's semi-streaming matching algorithm. These streaming approaches are compared against the state-of-the-art 3/2-approximate offline algorithm. The semi-streaming matching and the novel edge cover algorithms proposed in this paper can process graphs with several billions of edges in under 30 minutes using 6 GB of memory, which is at least an order of magnitude improvement from the offline (non-streaming) algorithms. For the largest graph, the best alternative offline parallel approximation algorithm (GPA+ROMA) could not finish in three hours even while employing hundreds of processors and 1 TB of memory. We also demonstrate an application of the semi-streaming algorithm by computing a matching using linearly bounded memory on item intersection graphs derived from three machine learning datasets, whereas the existing offline algorithms could not complete on one of these datasets since their memory requirements exceeded 1TB.

Ferdous, S M.↗