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Optimize with a SATA RAID Storage Solution
Range of capacities as low as $1250 per TB. Ideal if you currently rely on servers/disks/JBODs
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This approach is well-proven in academic and specialized industrial applications, and is also biologically plausible. Say what? As you read this article, you are processing and understanding it using the most complex parallel piece of hardware known to man—your brain (a BNN, or biological neural network, composed of 100 billion simple processing units). ANNs (artificial neural networks) represent a branch of AI (artificial intelligence) dedicated to reproducing the characteristics of BNNs.
So in summary then, we want to—in fact we need to—parallelize hardware if we are to "kick on" and provide more computing resources in a sustainable way to software applications.
Let's now briefly look (an in-depth look would be a meaty article in its own right) at how the main hardware vendors have commoditized parallel hardware architectures. Essentially, every hardware vendor has had to create its own viewpoint on how to support parallelism in hardware, implement it, and then pass it to its marketing department to explain how much better its secret sauce is over the opposition's. The challenge for system designers is to select the right hardware platform for their production environment. The challenge for programmers, especially the team of programmers who develop the JDK for these platforms is two-fold:
java.lang.Thread, but remain true to WORA?
C and C++ programmers have the luxury of being able to provide architecture-specific flags to GCC (GNU Compiler Collection) or g++ (GNU C++) to optimize for those architectures. Java programmers have no such compile-time luxury. Instead, we must tune the running system itself, often using the JVM's -XX flags (see Resources) to tweak behavior for the actual platform used.
Now, let's examine the main characteristics of the hardware offerings from the main vendors.
Intel's offering was once synonymous with the concept of hyperthreading (see Resources for more information), but has now matured to include true multicore support—for example, the dual core CPU now shipping inside the new Apple MacBook. Initially, Intel's implementation of hyperthreading replicated some parts of the CPU architecture, but retained single copies of others, creating bottlenecks under certain conditions. This type of implementation must be regarded as a stop-gap measure at best, designed to keep the market happy while the true multicore support (two cores at first) filtered into production. Intel plans to release a four-core CPU next year for the server market.
See Resources for more details on Intel's offerings, as well as offerings for the other vendors mentioned here.
AMD (Advanced Micro Devices) offers dual-core CPUs. In addition, it uses its Direct Connect and HyperTransport technologies to team those CPUs together to create larger clusters of CPUs.
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