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@@ -35,92 +35,73 @@ <h1><a href="index.html">PanDA WMS</a></h1> | |
| </nav> | ||
| </header> | ||
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| <!-- Banner --> | ||
| <section id="banner"> | ||
| <div class="inner"> | ||
| <!-- PanDA Logo --> | ||
| <h2><img src="images/PanDA-rev-logo2.png" style="width: 800px;"/></h2> | ||
| <p>Your Gateway to Efficient Distributed Computing</p> | ||
| </div> | ||
| <a href="#one" class="more scrolly">Learn More</a> | ||
| </section> | ||
| <!-- One --> | ||
| <section id="one" class="wrapper style1 special"> | ||
| <div class="inner"> | ||
| <header class="major"> | ||
| <h2>Empower Your Research with PanDA WMS</h2> | ||
| <p>In the world of scientific research and data-driven discoveries, time is of the essence. PanDA WMS empowers researchers, academic institutions, and scientific experiments to harness the full potential of distributed computing. By automating task distribution and resource management, PanDA accelerates your data analysis, simulations, and computations, leading to faster results and accelerated scientific breakthroughs.</p> | ||
| <h2>Team</h2> | ||
| <p>Our team is distributed across the atlantic. The majority of the team is based at CERN in Geneva, Switzerland with some team members being based at BNL in Long Island, NY, USA.</p> | ||
| </header> | ||
| <!-- <ul class="icons major">--> | ||
| <!-- <li><span class="icon fa-gem major style1"><span class="label">Intelligent Workload Distribution</span></span></li>--> | ||
| <!-- <li><span class="icon fa-heart major style2"><span class="label">Scalability and Flexibility</span></span></li>--> | ||
| <!-- <li><span class="icon solid fa-code major style3"><span class="label">Fault Tolerance and Resilience</span></span></li>--> | ||
| <!-- <li><span class="icon fa-flag major style4"><span class="label">Trusted by Leading Scientific Experiments</span></span></li>--> | ||
| <!-- </ul>--> | ||
| <p>PanDA WMS is a highly scalable and flexible workload management system that was initially developed to address the computational challenges of the ATLAS experiment at CERN. PanDA has demonstrated its capability to manage 24x365 processing on approximately 800,000 concurrent cores globally for ATLAS, encompassing all types of workflows, resource types, and a large user base.</p> | ||
| <p>It has become a trusted solution utilized by various scientific experiments and projects worldwide, providing efficient distributed computing and resource optimization for researchers across disciplines.</p> | ||
| <p>PanDA WMS is trusted by some of the world's most prominent scientific experiments and projects: <a href="https://atlas.cern/" target="_blank">ATLAS</a> at <a href="https://home.cern/" target="_blank">CERN</a>, <a href="https://wwwcompass.cern.ch/compass/" target="_blank">COMPASS</a> at <a href="https://home.cern/" target="_blank">CERN</a>, <a href="https://www.sphenix.bnl.gov/" target="_blank">sPHENIX</a> at <a href="https://bnl.gov/" target="_blank">BNL</a> and <a href="https://rubinobservatory.org/" target="_blank">Vera C. Rubin Observatory</a> in Chile.<p> | ||
| </div> | ||
| </section> | ||
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| <!-- Two --> | ||
| <section id="two" class="wrapper alt style2"> | ||
| <section class="spotlight"> | ||
| <div class="image"><img src="images/pic01.jpg" alt="" /></div> | ||
| <div class="content"> | ||
| <h2>Efficient Workload Distribution</h2> | ||
| <p>PanDA's intelligent algorithms ensure that computing tasks are distributed optimally across available resources, maximizing throughput and minimizing processing time. It saves valuable time and computing resources, enabling researchers to focus on analysis and results rather than managing the computational infrastructure.</p> | ||
| </div> | ||
| </section> | ||
| <section class="spotlight"> | ||
| <div class="image"><img src="images/pic02.jpg" alt="" /></div> | ||
| <div class="content"> | ||
| <h2>Scalable and Flexible</h2> | ||
| <p>Whether you're working with a small cluster or a massive grid, PanDA can seamlessly scale to meet your computational demands, ensuring a smooth workflow. Its flexibility allows it to adapt to various computing environments, making it an ideal solution for diverse research fields and projects.</p> | ||
| </div> | ||
| </section> | ||
| <section class="spotlight"> | ||
| <div class="image"><img src="images/pic03.jpg" alt="" /></div> | ||
| <div class="content"> | ||
| <h2>Fault Tolerant and Resilient</h2> | ||
| <p>PanDA's fault-tolerant design ensures uninterrupted processing, automatically handling and recovering from failures, and avoiding data loss. It guarantees a reliable computing environment, minimizing disruptions and maximizing efficiency in your research tasks.</p> | ||
| </div> | ||
| </section> | ||
| </section> | ||
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| <!-- Three --> | ||
| <section id="three" class="wrapper style3 special"> | ||
| <section id="two" class="wrapper style3 special"> | ||
| <div class="inner"> | ||
| <header class="major"> | ||
| <h2>Why Choose PanDA WMS?</h2> | ||
| <p>Time efficiency, cost-effectiveness, a vibrant community, and a proven track record are among the many reasons to choose PanDA WMS for your distributed computing needs.</p> | ||
| <h2><a class="icon solid fa-robot"></a> Current members in alphabetic order <a class="icon solid fa-robot"></a></h2> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Aleksandr ALEKSEEV <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/Foorth"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Fernando Harald BARREIRO MEGINO <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/fbarreir"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/fernando-h-b-281a3144/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Kaushik DE <a class="icon solid fa-envelope" href="mailto:[email protected]"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Wen GUAN <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/wguanicedew"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/wen-guan-55029b78/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Edward KARAVAKIS <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/EdwardKaravakis"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/edwardkaravakis/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Alexei KLIMENTOV <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/alexeiklimentov"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Tatiana KORCHUGANOVA <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/tkorchug"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/tatiana-korchuganova-625057182/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Fa-Hui LIN <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/mightqxc"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Tadashi MAENO <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/tmaeno"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/tadashi-maeno-7a424a280/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Paul NILSSON <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/PalNilsson"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/paulnilsson/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Torre WENAUS <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/wenaus"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/torre-wenaus-2308b190/"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Zhaoyu YANG <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/zhaoyuyoung"></a></h4></br> | ||
| </p> | ||
| <p> | ||
| <span style="width: 200px"></span> | ||
| <h4>Xin ZHAO <a class="icon solid fa-envelope" href="mailto:[email protected]"></a> <a class="icon brands fa-github" href="https://github.com/xzhao87"></a> <a class="icon brands fa-linkedin" href="https://www.linkedin.com/in/xin-zhao-41094594/"></a></h4></br> | ||
| </p> | ||
| </header> | ||
| <ul class="features"> | ||
| <li class="icon solid fa-clock"> | ||
| <h3>Time Efficiency</h3> | ||
| <p>PanDA significantly reduces time-to-solution for complex computations, enabling researchers to focus on analysis and results rather than managing the computational infrastructure.</p> | ||
| </li> | ||
| <li class="icon solid fa-laptop"> | ||
| <h3>Cost-Effective</h3> | ||
| <p>By optimizing resource usage and properly utilizing available resources, PanDA helps organizations save on computing costs and achieve better return on investment.</p> | ||
| </li> | ||
| <li class="icon solid fa-code"> | ||
| <h3>Community and Collaboration</h3> | ||
| <p>Join a vibrant community of researchers and developers who actively contribute to the continuous improvement of PanDA, fostering collaboration and knowledge exchange. PanDA is an Open Source project.</p> | ||
| </li> | ||
| <li class="icon solid fa-check"> | ||
| <h3>Proven Track Record</h3> | ||
| <p>PanDA WMS has been successfully deployed and utilized in cutting-edge scientific experiments and large-scale projects, making it a trusted solution for distributed computing.</p> | ||
| </li> | ||
| <li class="icon solid fa-gem"> | ||
| <h3>Scalability and Flexibility</h3> | ||
| <p>PanDA's scalable and flexible design allows it to adapt to various computing environments, making it an ideal solution for diverse research fields and projects.</p> | ||
| </li> | ||
| <li class="icon solid fa-shield-alt"> | ||
| <h3>Fault Tolerance and Resilience</h3> | ||
| <p>PanDA's fault-tolerant design ensures uninterrupted processing, automatically handling and recovering from failures, and avoiding data loss.</p> | ||
| </li> | ||
| </ul> | ||
| </div> | ||
| </section> | ||
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