Will the mountain come to the Mohammad or Mohammad go to the mountain?

When we consider whether clouds can provide a suitable platform for high performance computing (HPC) we always talk about how cloud computing needs to evolve to suit the needs of HPC – in other words will the mountain come to Mohammad. But there are signs that there may also be movement in the other direction – transforming HPC so that it may work better in the cloud paradigm. Mohammad may have to go.

Happy Thanksgiving!

Last week (Nov 13-19) was the annual Supercomputing (SC) conference. This year it was held in New Orleans, Louisiana. Cloud computing was featured by vendors and speakers throughout the conference. There were far too many cool products, talks, and papers to mention in a single post, however, a few of the highlights that we are thankful we caught in person include:

• Two representatives from Platform Computing presented a large-scale cloud deployment being tested at the CERN laboratory in “Building the World’s Largest HPC Cloud.” CERN is testing Platform ISF to run scientific jobs in a virtualized environment. Results included reports of launching several thousand VMs and a comparison of image distribution techniques.
• In “Virtualization for HPC”, members of the academic (Ohio State University, ORNL) and industrial (VMware, Univa UD, Deopli) communities shared their vision of a future for virtualization technologies in HPC. Topics discussed included pro-active fault tolerance using migration, virtualized access to high-performance interconnects, and new hypervisors technologies designed for exascale computing.
• In “Low Latency, High Throughput, RDMA and the Cloud in Between” representatives from Mellanox, Dell, and AMD discussed the advantages of cloud computing and highlighted the importance of reducing latency and increasing throughput for scientific communities. RDMA over Converged Ethernet (RoCE) was emphasized as a specific effort toward reducing latency in virtualized environments.
• The work in “Elastic Cloud Caches for Accelerating Service-Oriented Computations” demonstrated a dynamic and fast memory-based cache using IaaS resources, specifically for a geoinformatics cyberinfrastructure. The system responds to changes in demand by dynamically adding or removing IaaS nodes from the cache.

In addition to some great cloud computing talks and sessions, cloud resources were also involved in a handful of demos and tutorials. In particular, Purdue demoed Springboard, a “hub” to work with NSF’s TeraGrid infrastructure. The hub provides a central point for researchers to collaborate and removes the need for researchers to rely strictly on the command line when interacting with the TeraGrid’s resources. Springboard also interfaces with the TeraGrid’s first cloud resource, Wispy, at Purdue. The National Center for Atmospheric Research (NCAR) and the University of Colorado at Boulder used 150 Amazon EC2 instances for the Linux Cluster Construction tutorial. The virtual machines were launched on-demand the morning of the tutorial. They provided participants with a realistic software environment for configuring and deploying a Linux cluster using a variety of open source tools such as OpenMPI, Torque, and Ganglia.

Amazon’s S3 is a great storage cloud service.  It provides highly available access to highly reliable storage at a price.  It has emerged as the data storage cloud de-facto standard for good reasons.  Its REST API has allowed several 3rd party software vendors to make impressive clients, both GUI and command line.

However, it is closed source and unavailable to the numerous data centers actively used for science.  Can private data cloud providers that already have substantial amounts of hardware allow their users to take advantage of the known interfaces and existing clients?  Can the Scientific community that already has access to vast amounts of computing and storage power, but not access to an expense accounts take advantage of these data storage cloud innovations?

I’ve been wanting to say a few words about sky computing for a while and eventually iSGTW forestalled me with a very nice article on the topic. It describes a cool work by Nimbus committer Pierre Riteau who created a virtual cluster of over a thousand cores over resources leased from six Nimbus clouds: three provided by Grid’5000 and three by FutureGrid.

“Sky computing” was a name we coined back in 2008 to describe the idea of operating in a multi-cloud environment. It addresses the issues of provider interoperability and comparison between providers (cloud markets) as well as end-user concerns – the abstractions and tools required to provide an integrated and secure environment over resources provisioned in multiple potentially distributed clouds which the original paper focused on.

Today we made open source Infrastructure-as-a-Service capabilities for science just a little bit better… The new Nimbus 2.6 makes your images get to the nodes a lot faster and provides capabilities allowing administrators to easily and dynamically shift resources between their tried-and-true batch scheduled cluster and a cloud — depending on where they need them more. The Nimbus Context Broker gets shiny new REST-based interfaces, which opens it up to new applications.

Right on the heels of Amazon’s groundbreaking news on the Cluster Compute instances a couple of days ago, comes this announcement about a partnership between CENIC, Pacific NorthWest GigaPoP (PNWGP), and Amazon: two 10 Gigabit per second (Gbps) connections to Amazon S3 and EC2. This connection will be available to CENIC and PNWGP member institutions (educational and research institutions on the West Coast and in the Pacific North-West) — among others, the many ocean scientists of the Ocean Observatory Initiative (OOI) who we are working with to develop cloud-based scientific infrastructure.

We all woke up to a game-changing announcement today: Amazon announced Cluster Compute instances designed to support the kinds of closely coupled workloads that high performance computing (HPC) relies on. The Cluster Compute instances consist of a pair of quad-core Intel “Nehalem” processors with 23 GB of RAM, and 1690 GB of local instance storage. But by far the best part of the offering is the 10 Gbps network that connects Cluster Compute instances — essential for HPC applications.

The issue of how exactly cloud computing differs from grid computing was responsible for much controversy in the last year. Here are my two cents on how Infrastructure-as-a-Service (IaaS) cloud computing and grid computing are different (also discussed in the Sky Computing paper)

At some level, both cloud computing and grid computing represent the idea of using remote resources. However, grid computing is built on the assumption that control over the manner in which resources are used stays with the site, reflecting local software and policy choices. These choices are not always useful to remote users who might need a different operating system, or login access instead of a batch scheduler interface to a site. Reconciling those choices between multiple groups of users proved to be complex, time-consuming, and expensive. Looking back, leaving complete control over the resources with the site was a pragmatic choice that enabled very fast adoption of a radically transformative technology. On the other hand, once the technology became successful, this factor made it difficult for it to scale to many user groups with different (and sometimes conflicting) requirements of what the resource should provide.

Amazon AWS recently announced that EC2 instances can be configured to launch from EBS volumes instead of bundled disk images.

Science users launching heterogeneous clusters can possibly take advantage of this in order to streamline the bundling of images. Those clusters often share a base image layout. Because these AMIs can now reference any number of EBS volumes in their external description including for the root disk, you can now work on customizing each partition and “mix and match” root disks and partitions more easily to make a cohesive cluster. That’s more convenient than maintaining such a partition organization separately and bundling images for each cluster node type, which is traditionally time consuming.

I recently attended an immensely interesting workshop on using cloud computing for systems biology computations. The workshop was co-held with SC09. The agenda and the presentations are available online from the workshop pages and are well worth a look. Here are some impressions from the workshop.

The workshop began with a discussion of current challenges in biosciences. One of the most compelling is personal medicine which helps physicians tailor treatments to individual patients based on feedback obtained on genetic and molecular level. For example, knowledge of genetic variations can now help physicians better assess treatment risks, manage dosing of drugs, better detect diseases in early stages and optimize treatments such as e.g., breast cancer therapy. In his introductory talk, Eugene Kolker said that today there were already hundreds of patients treated based on information obtained from their genetic signatures as part of experimental programs. He also emphasized that the main obstacle to progress in this area is not obtaining the data but the response time and ability to store, process, and analyze it to obtain the right information. And this brings us to cloud computing, in this workshop the “prime suspect” to process, analyze and store on demand.