IMDGs: Next Generation Parallel Supercomputers

01.18.14

Topics : Architecture, Technology

In many ways, in-memory data grids (IMDGs) are next generation parallel supercomputers. That sounds like a big claim, but let’s take a look some history which shows us the common computing architecture that underlies both technologies.

Pioneering Technology from Caltech

Back in the 1980s, IBM, Intel, and nCube (among others) began commercializing parallel computing (“multicomputing”) technology pioneered by professors Charles Seitz and Geoffrey Fox at Caltech. They recognized that commodity servers could be clustered using a high speed network to run parallel programs which deliver highly scalable performance well beyond the power of shared memory multiprocessing servers. With the development of message passing libraries, these multicomputers were programmed using C and Fortran to implement parallel applications in matrix algebra, structural mechanics, fluid dynamics, distributed simulation, and many other areas.

While this multicomputing architecture had the potential to deliver very high scalability, it introduced several challenges. Chief among them was hiding network overhead and latency which could easily dominate processing time and impede scalability. Hardware architects developed novel high speed networks, such as Bill Dally’s pipelined torus and mesh routers, to minimize message passing latency. (Standard 10 Mbps Ethernet LANs of the 1980s were quickly determined to be too slow for use in multicomputers.)

Achieving Scalable Speedup

However, to really deliver scalable performance, Cleve Moler (the creator of Matlab, then working at Intel)– and, independently, John Gustafson at Sandia Labs – recognized that scaling the size of an application (e.g., the size of a matrix being multiplied) as more servers are added to the cluster helps mask networking overhead and enable linear growth in performance; this is called Gustafson’s Law. At first glance, this insight might seem counter-intuitive since one expects that adding computing power will speed up processing for a fixed size application. (See Amdahl’s Law.) But adding servers to a computing cluster to handle larger problem sizes actually is very natural: for example, think about adding web servers to a farm as a site’s incoming web load grows.

Keeping It Simple with Data-Parallel Programming

The daunting complexity inherent in the creation of parallel programs with message passing posed another big obstacle for multicomputers. It became clear that just adding message passing APIs to “dusty deck” applications could easily lead to frustrating and inscrutable deadlocks. Developers realized that higher level design patterns were needed; two that emerged were the “task parallel” and “data parallel” approaches. Data-parallel programming is by far the simpler of the two, since the developer need not write application-specific synchronization code, which can be complex and error prone. Instead, the multicomputer executes a single, sequential method on a collection of data that has been distributed across the servers in the cluster. This code automatically runs in parallel across all servers to deliver scalable performance. (Of course, message passing may be needed between execution steps to exchange data between parts of the application.)

For example, consider a climate simulation model such as NCAR’s Community Climate Model. Climate models typically partition the atmosphere, land, and oceans into a grid of boxes and model each box independently using a sequential code. They repeatedly simulate each box’s behavior and exchange data between boxes at every time step in the simulation. Using a multicomputer, the boxes all can be held in memory and distributed across the servers in the cluster, thereby avoiding disk I/O which impedes performance. The cluster can be scaled to hold larger models with more boxes to improve resolution and generate more accurate results. The multicomputer provides scalable performance, and it runs data-parallel applications to help keep development as simple as possible.

IMDGs Use Parallel Computing Architecture

So what does all this have to do with in-memory data grids? IMDGs make use of the same parallel computing architecture as multicomputers. They host service processes on a clustered set of servers to hold application data which they spread across the servers. This data is stored as one or more collections of serialized objects, such as instances of Java, C#, or C++ objects, and accessed using simple create/read/update/delete (“CRUD”) APIs. As the data set grows in size, more servers can be added to the cluster to ensure that all data is held in memory and access throughput grows linearly.

By doing all of this, IMDGs keep access times constant, which is exactly the characteristic needed by applications which have to handle growing workloads. For example, consider a website holding shopping carts in an IMDG. As more and more customers are attracted to the site, web servers must be added to handle increasing traffic. Likewise, IMDG servers must be added to hold more shopping carts, scale access throughput, and keep response times low. In a real sense, the IMDG serves as a parallel supercomputer for hosting application data, delivering the same benefits as it does for climate models and other scientific applications.

IMDGs Run Data-Parallel Applications

However, the IMDG’s relationship to parallel supercomputers runs deeper than this. Some IMDGs can host data-parallel applications to update and analyze data stored on the grid’s servers. For example, ScaleOut Analytics Server uses its “parallel method invocation” (PMI) APIs to run Java, C#, or C++ methods on a collection of objects specified by a parallel query. It also uses this mechanism to execute Hadoop MapReduce applications with very low latency. In this way, the IMDG serves as a parallel supercomputer by directly running data-parallel applications. These applications can implement real-time analytics on live data, such as analyzing the effect of market fluctuations on a hedge fund’s financial holdings (more on that in an upcoming blog).

IMDGs Offer Next Generation Parallel Computing Techniques

IMDGs bring parallel supercomputing to the next generation in significant ways. Unlike multicomputers, they can be deployed on cloud infrastructures to take full advantage of the cloud’s elasticity. They host an object-oriented data storage model with property-based query that integrates seamlessly into the business logic of object-oriented applications. IMDGs automatically load balance stored data across all grid servers, ensuring scalable speedup and relieving the developer of this burden. They provide built-in high availability to ensure that both data and the results of a parallel computation are not lost if a server or network component fails. Lastly, they can ship code from the developer’s workstation to the grid’s servers and automatically stage the execution environment (e.g., a JVM or .NET runtime on every grid server) to simplify deployment.

Although they share a common heritage, IMDGs are not your parent’s parallel supercomputer. They represent the next generation in parallel computing: easily deployable in the cloud, object-oriented, elastic, highly available, and powerful enough to run data-parallel applications and deliver real-time results.

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