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April 27, 2007
Java Framework Catches the Multicore Wave
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There is classic conflict in database processing. More and more data is being generated, while users want to process that data in smaller and smaller timeframes. Fortunately, these two colliding demands have a possible solution -- parallel hardware. But getting the software to exploit that hardware has been a struggle.
Several years ago, Pervasive Software realized that their own database technology was not going to scale into the 21st century. And as their customers took on commercial high performance computing applications, they too would be searching for ways to deal with the data deluge. According to Pervasive CTO Mike Hoskins, the industry is now at a fork in the road for how high performance computing is going to proceed. Although the last 20 years has seen the rise of cluster and grid architectures, Hoskins believes the advent of multicore technology will now favor scaled-up SMP architectures for many types of HPC applications.
"This is just the beginning of a revolution that I think will forever change not just how high performance scientific computing is done, but how any kind of high performance computing is done," says Hoskins. "Significantly, since these SMP machines use a single address space, we can enjoy the other revolution that is going on, which is the software revolution. This has been largely absent in the HPC world where they're still wrestling with Stone Age technologies like MPI and other brittle technologies that try to organize coherence across non-uniform memory and heterogeneous clusters."
Hoskins says what software developers really want is familiar programming languages, like Java, and a global address space. He thinks that over the next five years there's going to be a movement away from loosely coupled grid and cluster platforms and toward SMPs. Hoskins speculates that scaled out cluster systems will only be used for the largest types of problems that can be parallelized relatively easily. For everyday high performance computing, he says, people will use multicore SMP platforms.
To meet the demands of multicore-based data processing, Pervasive came up with DataRush, a Java framework for data- and compute-intensive applications -- what Hoskins calls "hyper-parallel" applications. The framework was conceived six years ago at a company called Data Junction, where Hoskins was the president. Data Junction was acquired in 2003 by Pervasive, where DataRush technology was further developed and refined.
The DataRush framework is based on a dataflow or directed graph model. Workloads that must process large datasets and can be represented as a dataflow graph, for example, data mining, searching/surveillance, and analytics, are good candidates for the technology. This includes applications like financial data auditing, genomic data analysis and logistics data processing.
The framework provides a high-level XML scripting interface for the programmer to specify how their data is to be processed. The DataRush SDK library provides a set of components, which includes standard data management operations like Sort, Join and Merge; prebuilt operators like And, Not, and Compare; and readers/writers for I/O. Users can extend the framework with custom components. Behind the scenes, the DataRush parallel processing engine performs all the necessary low-level concurrency by managing threads and global memory accesses.
From the developer's point of view, everything is done with standard vanilla Java; no special interpreters or Java Virtual Machines (JVMs) are required. The technology exploits the fact that JVMs are inherently thread-aware and are implemented on all commercial multicore platforms. Thus, once the application is developed in the framework, parallelization on the hardware becomes automatic. An interesting side-effect of JVM independence is that even if Pervasive is overly optimistic about the rise of SMP systems, Terracotta's cluster JVM could be used by DataRush to parallelize applications in a more loosely coupled environment.
Today, many dataflow-type applications are being implemented using either a cluster/MPI model or in a global address space environment using conventional languages, but without a framework to hide the concurrency primitives. Hoskins says these approaches lead to a lot of hard-to-maintain code that is expensive to develop and won't scale well when more highly parallelized hardware become available.
"A lot of the supercomputing industry is stuck in a bit of a time warp," says Hoskins. "I started with mainframes and assembly programming. In those days machines were expensive and humans were cheap. Now, it's turned around completely. The constant focus on machine performance really misses the boat."
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