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  1. <p><a class="asset-img-link" href="http://onlinedigeditions.com/publication/frame.php?i=144258&p=&pn=&ver=flex" style="float: right;" target="_self"><img alt="BiophysJ-bestof2012-cover" border="0" class="asset asset-image at-xid-6a00e54ef157d78834017d4162de4e970c" src="http://folding.typepad.com/.a/6a00e54ef157d78834017d4162de4e970c-800wi" style="margin: 0px 0px 5px 5px;" title="BiophysJ-bestof2012-cover" /></a>Biophysical Journal announced their "<a href="http://onlinedigeditions.com/publication/frame.php?i=144258&p=&pn=&ver=flex" target="_self">Best of 2012</a>" paper collection. We were excited that <a href="http://www.ncbi.nlm.nih.gov/pubmed/22385857" target="_self">one of our papers was included</a>. That work, "Protein Folding is Mechanistically Robust" investigates how key aspects of FAH technology (MSMs) can yield new insights into protein folding in some unexpected ways. Congratulations to Jeffery Weber for his work. We've posted the technical abstract below as well.</p> <div> <p><strong>ABSTRACT. </strong>Markov state models (MSMs) have proven to be useful tools in simulating large and slowly-relaxing biological systems like proteins. MSMs model proteins through dynamics on a discrete-state energy landscape, allowing molecules to effectively sample large regions of phase space. In this work, we use aspects of MSMs to ask: is protein folding mechanistically robust? We first provide a definition of mechanism in the context of Markovian models, and we later use perturbation theory and the concept of parametric sloppiness to show that parts of the MSM eigenspectrum are resistant to perturbation. We introduce a new, to our knowledge, Bayesian metric by which eigenspectrum robustness can be evaluated, and we discuss the implications of mechanistic robustness and possible new applications of MSMs to understanding biophysical phenomena.</p> </div> <p> </p> Voir l'article complet
  2. <p>Folding@home has historically been an extremely powerful computing resource, hitting many major computing milestones. For example, we were awarded a <strong><a href="http://www.engadget.com/2007/10/31/folding-home-recognized-by-guinness-world-records/" target="_self">Guinness World Record</a></strong> for being the first to reach a petaflop (10^15 floating point operations per second).</p> <p>We keep track of the power of Folding@home on our <strong><a href="http://fah-web.stanford.edu/cgi-bin/main.py?qtype=osstats2" target="_self">osstats page</a></strong>. Recently, we found an issue with our reporting. In particular, we were investigating a major under-reporting (OSX stats reported that there were no OSX machines at all). A few months ago, Christian Schwantes (FAH Team member) rewrote our main stats scripts and he fixed a very old bug in the stats reporting. That bug had a workaround in the osstats2 page, so now the workaround was failing and that led to finally getting osstats2 working. <br /><br />After checking the db with the raw files from the work servers (it's an important check that we can know what the right answer is from this more laborious manual process of going through the WS logs), this result is now correct, fixing the previous under-reporting. We also now are keeping better track of the Fermi (and later) class of NV GPUs. We were under-reporting their FLOP count and that alone was what make a huge difference in the FLOPs reported.</p> <p>The upshot of all of this is that we now are no longer under-reporting our stats and the reported FLOPs has gone up significantly, to about 12 PFLOPs. This puts Folding@home past the 10 PetaFLOP scale, which is itself a very significant amount of computer power.</p> <p>For more information, please see our <a href="http://folding.stanford.edu/English/FAQ-flops">FLOPS FAQ</a>, whichis useful in discussing this topic, which is surprisingly complicated.</p> Voir l'article complet
  3. <p>We've rolled out the latest version 7.3 client to the main web site. This new client should be much easier to install and comes with a new web interface which is simpler and easier to use. </p> <p>But in addition, we've been trying to communicate our goals and mission more clearly. Our first steps there are a new web site and a new video. The video in particular is something that we're all very excited about, since it explains to a broad audience what FAH is all about.</p> <p><iframe frameborder="0" height="281" src="http://www.youtube.com/embed/7sJx9z1uB9k?feature=oembed" width="500"></iframe> </p> Voir l'article complet
  4. <p>Guest post from Dr. Greg Bowman, University of California, Berkeley</p> <p> </p> <p>Markov state models (MSMs) are a powerful approach for investigating the dynamics of proteins and other biomolecules. The Folding@home team has helped to pioneer the development of these methods and continues to make important contributions to their further development. For example, the Huang lab at the Hong Kong University of Science and Technology has created an exciting new method for coarse-graining Markov models (the paper is available <a href="http://arxiv.org/abs/1301.0974">here</a>).</p> One of the major challenges in this area is that the high-resolution Markov models capable of making quantitative predictions of experiments often have tens of thousands of parts. As you can imagine, it is hard to look at each of these parts and the interactions between them to understand the model. Therefore, it is valuable to create a new model with, say, a dozen states that still captures much of the behavior of the more complex model. <p>The new method from the Huang lab makes use of a mathematical principle called a Nystrom expansion to build more accurate coarse-grained models. The key advantage of this approach is that one can quickly identify the most important pieces of a model and prove that the remaining pieces can safely be ignored (or merged into the more important parts). As a proof of principle, the Huang lab has shown they can build much better models for a few small proteins than is possible with previous methods. </p> <p> </p> <p> </p> Voir l'article complet
  5. <p>We're having an issue with the interaction between our web main server and stats db. We are looking into this. It looks like the obvious fixes haven't worked, so we are escalating. </p> <p>The stats are being input fine, it's just with the reporting on the main web page. If this continues, we may take the main web page stats pages down, but 3rd party stats should still work and be regularly updated.</p> <p>As always, we'll update you on our progress with this issue.</p> Voir l'article complet
  6. <p>We have a VM server go down which brought down a few FAH services (notably web site and backup AS), but it's back up. The stats viewing was unavailable during the outage, but the stats input to the db was unaffected.</p> <p>Our sysadmins got the machine back up and all looks to be back in order.</p> Voir l'article complet
  7. <p>For those of you interested in the older 6.41 client, we've updated the download links for the 6.41 GPU client. Please find it in the normal place:</p> <p>http://folding.stanford.edu/English/DownloadWinOther</p> <p>This is likely only of interest to a very small community as we highly recommend most people to use the latest v7 client.</p> <p> </p> <table bgcolor="#FFEEFF" border="0" cellpadding="0" cellspacing="0"> <tbody> <tr> <td valign="top"><strong>Windows XP/2003/Vista/7 GPU3</strong> (required for Fermi) System tray client with special viewer for NVIDIA GPU's (installer msi). <br /><br /></td> <td valign="top">6.41</td> </tr> </tbody> </table> <table bgcolor="#FFEEFF" border="0" cellpadding="0" cellspacing="0"> <tbody> <tr> <td valign="top"><strong>Windows XP/2003/Vista/7 GPU3</strong> (required for Fermi) <em>no-nonsense</em> console client. <br /><br /></td> <td valign="top">6.41</td> </tr> </tbody> </table> <p> </p> Voir l'article complet
  8. We have had an unfilled spot in our GPU programming team for a few months and I'm happy to announce that we recently made a great new hire: Yutong Zhao. Yutong completed his undergraduate degree in Math, Chemistry, and Biochemistry from the University of Toronto, and a Masters degree in Computational Chemistry from HKUST, focusing on GPU-powered clustering algorithms. Previously, he has also worked on projects such as FoldIt! Currently, he is the lead developer of the Folding@Home GPU core and aims to extend functionality while increasing maintainability. He also plays a key role on the OpenMM side, he works on devising parallel algorithms to speed up MD simulations. He also maintains a blog detailing some of his algorithms and problems on www.proteneer.com . Voir l'article complet
  9. We've put a new revised AS up and we think this should fix the recent issues. We will of course continue to monitor the situation. Voir l'article complet
  10. We've been working to fix two issues with GPU clients in FAH. Here's an update: 1) There was an issue with NVIDIA clients with G80 GPUs getting stuck in a core_outdated download infinite loop. This has now been resolved. This was caused when we stopped older projects which used core_11 (which the G80's were primarily running) and now the G80's are directed to more recent projects. However, with the ending of the old projects and the start of the new ones, that means G80 GPUs go to core_15, which has much more strict testing for GPU memory errors. Several donors have reported that these tests are failing with their boards. We are looking into whether this is an issue with our test or potentially that the boards are not capable of running the latest core. So, while the core_outdated loop issue appears now to be fixed, there is another unresolved issue which we are continuing to look into. 2) There was a second issue with ATI clients getting directed to servers running NV WU's. This error is also unresolved at the moment, but we have a handle on what's happening on our end and have a team working on it. I don't have any news here to report other than we can see the issue cause in our logs, which is the first step to fixing it. Voir l'article complet
  11. We've been seeing donor reports of WU assignment issues for GPUs (WU's intended only for G80 GPUs going to Fermi and Kepler). We finished an AS patch yesterday, gave it a day of testing, then rolled it out this morning to address this issue. We generally don't like to roll out new AS code this quickly, but this time it seemed important to address this issue quickly. We hope that as of 7:30am pacific time, this issue is now addressed, but we'll be keeping an eye out during the day to see. Voir l'article complet
  12. The unified GPU/SMP benchmarking scheme will be rolled out today on FAH. All the future GPU projects would now be benchmarked using the new scheme. Most of the existing GPU projects have been re-benchmarked to reflect the changes in the benchmarking scheme. We are still in the process of re-benchmarking some old projects assigned to ATI and G80 GPUs. The uniform benchmarking scheme significantly boosts the base points for all GPU projects. However, Quick Return Bonus for the GPU clients has not been introduced at this stage, but will be introduced once we work out an issue on our side. We would like to thank all the beta testers who participated in testing the unified benchmarking scheme. Voir l'article complet
  13. Guest post from Dr. Gregory Bowman, UC Berkeley Ken Dill, a long-standing leader in protein folding, justpublished a beautiful review article about progress in the field. The main point is that what began as aspecific research question has now evolved into an entire field with numerousresearch directions. It’s would bedifficult to provide a more eloquent explanation than Ken’s, so here are a fewexcerpts from the article “The protein-folding problem was firstposed about one half-century ago. The term refers to three broad questions: (i)What is the physical code by which an amino acid sequence dictates a protein’s native structure? (ii) How can proteins fold so fast? (iii) Can we devise acomputer algorithm to predict protein structures from their sequences?... computer simulations of the physical forcesin chemically detailed models have now achieved the accurate folding of smallproteins… What began as three questionsof basic science one half-century ago has now grown into the full-fledged research field of protein physical science.” Insights and achievements fromFolding@home are highlighted on a number of occasions throughout the article. So, congrats to all for helping to establishthis ever-growing field. Also, there's a nice video about this online for those who are interested Voir l'article complet
  14. We have been updating our internal accounting of stats for FLOPs for FAH. The major revamp has come from a better handling of the difference between a CPU and a core. Modern CPUs have multiple cores. Before the v7 client, it was common for donors to fold with multiple uni-processor clients, one for each core. Now, with v7 making SMP more easy to run, SMP is much more common. This means we need to keep track of CPUs and cores more explicitly. Our new serverstats page takes care of this and also updates for estimates for FLOPs across the board, but especially from GPUs. Our goal in general is to be conservative with this reporting, but the old page was likely too conservative. The end result of the update is more accurate representation of the power of FAH today. The new page is located at: http://fah-web.stanford.edu/cgi-bin/main.py?qtype=osstats2 Voir l'article complet
  15. One of our servers (VSP12) which runs multiple virtual servers (VSP12a through g) will be take down for maintenance in the afternoon (Nov 20, around 1:00 pm) and is planned to be back up later in the evening. All the interfaces on this machine (Vsp12a,b,c,d,e,f,g with server addresses, 59, 60, 141, 142, 143, 144) hosting projects with ID in the range 8001-8067 will be affected. This includes several GPU3 and SMP A4 (multi+uniprocessor) projects. This server hosts a large number of current FAH projects but donors would be able to get work units from other servers with similar work units. Voir l'article complet
  16. One of our key server rooms will undergo network maintenance on Saturday, November 10th, from 5AM to 8AM pacific time. During the maintenance window, we expect that the servers in that room will be unreachable, hopefully for only 30 minutes each, but potentially for the full time range. We note that this is only one of our server rooms, so the FAH backend should still be primarily operational, but some donors will see some issues with returning work during this time. We also stress that the FAH server backend is architected such that even when servers are down, the points for donors will not be lost, and just the accounting for those points will be delayed until the servers are back up. Voir l'article complet
  17. We have several on-going software development efforts and I'd like to give donors an update. v7 client. Joe Coffland and his team have been working hard on new client releases. 7.2.9 has just been released and a new version will be undergoing beta testing soon. Moreover, we are continuing work on improving the v7 client for windows and squashing the remaining bugs. Moreover, there's additional effort in OSX due to the hiring of a programmer (Kevin Bernhagen) just for the OSX client, as well as additional work for smoother OSX and linux installs. Gromacs core. The Gromacs core team (Prof. Michael Shirts and Prof. Peter Kasson and their labs, at the University of Virginia) are working on the new cores based on the new version of gromacs (4.6). New OpenMM core. The OpenMM team at Stanford (Dr. Peter Eastman and Yutong Zhao) are working on speed improvements for OpenMM (the basis of the FAH GPU core) in general, but in particular optimizations for Kepler and AMD (in coordination with engineers at NVIDIA and AMD, respectively). Yutong has a new FAH GPU core working in the lab and we are doing internal testing on it. Since openMM is full open source, you can see more details, including a commit and change log, at the openMM web site (https://simtk.org/home/openmm). New FAH viral ad campaign. We're also working on a new landing page for FAH and a new video to advertise FAH. This new web/video campaign is coordinated with new client/installer changes to make FAH easier to install and run, especially for those new to Folding@home. We understand that donors don't get to see all of what's going behind the scenes, so we'll try to post these sorts of updates more frequently. Voir l'article complet
  18. For several years, we have worked closely with Sony to bring Folding@home to the PS3. We're excited about what we've been able to do. Since the PS3 started folding in 2007, we've done some really amazing things, with several announcements this year acknowledging advancements in Alzheimer's Disease, Cancer (and this link), Influenza, Type II Diabetes, and other new drug targets. We've come a long way in the last 5 years and we have a lot going on to continue our tradition of pushing the envelope into new technologies. Voir l'article complet
  19. The current benchmarking calculations for SMP and GPU projects are performed on different machines since originally the SMP cores could not perform the calculations that the GPUs cores could and vice versa (GPUs were only for implicit solvent calculations and SMP only for explicit solvent calculations). With recent advances in both cores and completion of our testing of these capabilities to ensure agreement, we are now confident we can do the same work on both cores. Thus, we feel that it is time to unify GPU and SMP benchmarking, both for simplicity and fairness. In order to complete the move towards this plan of "equal points for equal work," new GPU projects will be benchmarked using the existing SMP benchmarking scheme. Based on our internal tests, the end effect of this new, unified benchmarking scheme would boost the points for the GPU projects, both in terms of base points but also by bringing Quick Return Bonuses to GPU clients. In order to test the new scheme, we have started a GPU3 project (Project ID: 8057) and released it for beta testing. Once the benchmarking scheme has been tested, all the current GPU projects will be re-benchmarked to reflect the changes in the benchmarking scheme. Voir l'article complet
  20. Guest post from Profs Kasson and Shirts, UVA and Mr. Coffland A new version of Gromacs (4.6) is coming, and we’re working to bring it to Folding@home. The new code contains a number of improvements (more than you’d expect for a minor version number!), and we’ll post about some of the individual features as we go. Not all of them will be available on F@h immediately, as some will require substantial development work over the next few months. But some of the basics are new free energy methods from our very own Prof. Michael Shirts, new and slightly faster inner-loop code, and some important tweaks to parallelization. Free energy calculations allow us to calculate things like how tightly drugs bind to proteins and the strength of attraction between protein components when pulled apart. And you, of course, know what faster inner-loop code and better parallelization mean! Gromacs is an interesting piece of simulation software in that it’s heavily optimized both for single-computer performance (part of why we chose it for F@h in the first place) and for parallel scaling. A lot of codes choose to emphasize one or the other. But Gromacs tries to do both. That will have some interesting and useful implications for F@h particularly as we look at more and more cores on donor CPU’s (and things like GPU integration). That’s all for now; we’ll keep you posted on progress. Thanks! The 4.6 Core Team (Profs. Kasson, Shirts, and the indefatigable Mr. Coffland) Voir l'article complet
  21. This video is a year old (and we've previously posted the audio), but looking at this again, a lot is still relevant (and we didn't post the video url), so here it is in case people are curious: Host: Marc Pelletier Dr. Vijay Pande, Stanford's Director of Folding@home, details how the World's most powerful system models Alzheimer's and other human diseases. Guest: Dr. Vijay Pande We invite you to read, add to, and amend our show notes. Comments and suggestions on Futures in Biotech. Also thanks to Phil Pelletier and Will Hall for the great themes. Thanks to Cachefly for providing the bandwidth for this netcast. Running time: Voir l'article complet
  22. Guest post from Dr. Gregory Bowman, UC Berkeley Two general objectives of the Folding@home project are (1)to explain the molecular origins of existing experimental data and (2) toprovide new insights that will inspire the next generation of cutting edgeexperiments. We have made tremendousprogress in both areas, but particularly in the first area. Obtaining new insight is even more of an artand, therefore, less automatable. To help facilitate new insights, I recently developed aBayesian algorithm for coarse-graining our models. To explain, when we are studying someprocess—like the folding of a particular protein—we typically start by drawingon the computing resources you share with us to run extensive simulations ofthe process. Next, we build a Markovmodel from this data. As I’ve explainedpreviously, these models are something like maps of the conformational space aprotein explores. Specifically, theyenumerate conformations the protein can adopt, how likely the protein is toform each of these structures, and how long it takes to morph from onestructure to another. Typically, ourinitial models have tens of thousands of parameters and are capable ofcapturing fine details of the process at hand. Such models are superb for making a connection with experiments becausewe can capture all the little details that contribute to particularexperimental observations. However, theyare extremely hard to understand. Therefore, it is to our advantage to coarse-grain them. That is, we attempt to build a model withvery few parameters that is as close as possible to the original, complicatedmodel. If done properly, the new modelcan capture the essence of the phenomenon in a way that is easier for us towrap our minds around. Based on theunderstanding this new model provides, we can start to generate new hypothesesand then test them with our more complicated models and, ultimately, viaexperiment. Statistical uncertainty is a major hurdle in performing thissort of coarse-graining. For example, ifwe observe 100 transitions between a pair of conformations and each of thesetransitions is slow, then we can be pretty sure this is really a slowtransition. However, if we only observeanother transition once and it happens to occur slowly, who knows? It could be that it is really a slowtransition. On the other hand, it couldbe we just got unlucky. Existing methods for coarse-graining our Markov modelsassume we have enough data to accurately describe each transition. Therefore, they often pick up these poorlycharacterized transitions as being important (for protein folding, we typicallycare most about the slow steps, so slow and important are synonymous). The new method I’ve developed (describedhere) explicitly takes into account how many times a transition wasobserved. Therefore, it canappropriately place emphasis on the transitions we observed enough times totrust while disregarding the transitions we don’t trust. To accomplish this, I draw on Bayesianstatistics. I can’t do this subjectjustice here, but if you’re ever trying to make sense of data that you havevarying degrees of faith in, I highly recommend you look into Bayesian statistics. Voir l'article complet
  23. Guest post from Dr. Gregory Bowman, UC Berkeley We’ve been making a lot of progress with developing Markov state model (MSM) methods for analyzing the data we generate with the help of the FAH community. For those of you with a theory background, MSMs are just discrete-time master equation models. For everyone else, MSMs are a way of describing the conformational space a protein (or other biomolecule for that matter) explores as a set of states (i.e. distinct structures) and the transition rates between them. Much of the theory underlying these methods is quite old but their use has been limited by the challenges inherent to identifying a reasonable set of states. During my time in the Pande lab, I worked with Xuhui Huang (now at the Hong Kong University of Science and Technology) to develop new methods for building MSMs from the large data sets we generate with FAH. Together, we started an open source software package called MSMBuilder (here) to automate the process of building MSMs. Now a number of more recent additions to the Pande lab are helping Xuhui, Vijay, and me in continuing to develop the software. As we just released an update to MSMBuilder, I was looking back at some of our user statistics and was pleased to see how quickly our project is gaining traction. Since its initial release in 2009, there have been over 1,600 unique downloads of MSMBuilder. One cute feature of our webpage—provided by the SimTk software consortium at Stanford—is that you can go look where all of our users are (here). Its fun to see that MSMBuilder is being used on 5 continents. Maybe most importantly, MSMBuilder has been used in at least 40 publications to date. MSMBuilder is coming up at conferences with increasing frequency too, so I look forward to reporting back on our growth in another year or so. Voir l'article complet
  24. Guest post from Dr. Gregory Bowman, UC Berkeley Most rational drug design efforts assume the target protein exists in a single structure and that the structure of one region of the protein--called the active site--allows the protein to perform some function. Once this assumption is made, the only way to manipulate a protein’s activity is with inhibitors that bind the active site tightly enough to block it from performing its intended function. Unfortunately, this strategy only works for ~15% of proteins, greatly limiting the number of proteins we can manipulate for therapeutic purposes. In a recent article published in the Proceedings of the National Academy of Sciences (link), I showed that simulations run on Folding@home can reveal new ways of manipulating a protein's activity. Specifically, I start off by recognizing that proteins are actually flexible and then use Folding@home to enumerate the different conformations a protein adopts. I then use statistical analysis to find parts of the protein that can communicate with the active site through a process called allostery. These regions--called allosteric sites--are attractive drug targets as the binding of small molecules to them can be communicated to the active site, ultimately affecting activity. As a proof of principle, I showed that my approach can identify a known allosteric site in Beta-lactamase (see figure below). This protein is an important drug target because it can confer bacteria with antibiotic resistance by breaking down antibiotics like penicillin. I also use my approach to predict new allosteric sites in Beta-lactamase and two other proteins that play important roles in immune deficiencies and HIV. Now I'm performing experiments to test my predictions. It will require a lot more of your WUs, but I hope this type of approach can eventually lead to new pharmaceuticals. On the left is a structure of Beta-lactamase that most people would think of as the structure of this protein. However, the right shows a different structure with a drug (cyan) bound in a pocket that isn’t visible in the structure on the left. Binding of this drug somehow affects the structure near the active site (green). Using my approach, I’m able to start with the structure on the left and then predict the existence of the structure on the right and the allosteric site the drug is bound to. Voir l'article complet
  25. A brief update to our previous blog post on the A4 bonus: the bonus is now in effect. Voir l'article complet
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