Case study of architecture of vector processor - Advanced Computer Architecture & Computing - S.S. Jadhav - Google книги
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Case Study: More Efficient Numerical Simulation in Astrophysics
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Consumers want improved battery life, size, and weight for their laptops, tablets, and smartphones. Likewise data center power demands and cooling costs continue to rise. At the same time, we demand constantly improving performance to enable compelling new user experiences.
A Simulation Study of Decoupled Vector Architectures
We want to access our devices through more natural interfaces speech and gestureand photo essay photoshop want devices to manage ever-expanding volumes of data home movies, pictures, and a world of content available in the cloud.
To deliver these new user experiences, programmer productivity is another essential element that must be delivered.
It must be easy for software developers to tap into new capabilities by using familiar, powerful programming models. Finally, it is increasingly important that software be supported across a broad spectrum of devices.
To navigate this complex set of requirements, the computer industry needs a different approach — a more efficient approach to computer architecture.
We need an approach that promises to deliver improvement across all 4 of these vectors: But in the processor couple of decades, mainstream computer systems typically include other processing elements as well. The architecture prevalent is the graphics processing unit GPUoriginally designed to perform specialized graphics computations in parallel. Over time, GPUs have become more powerful and more generalized, allowing them to be applied to case purpose study computing vectors with excellent power efficiency.
But current CPUs and GPUs have been designed as separate processing elements and do not work together efficiently… Today, a growing number of mainstream applications require the high performance and power efficiency achievable only through such highly parallel computation. But current CPUs and GPUs have been designed as separate processing elements and do not work together efficiently — and are cumbersome to program.
Each has a separate memory space, requiring an application to explicitly copy data from CPU to GPU and then back again. A program running on the CPU queues work for the GPU using system calls through a device driver stack managed by a completely separate scheduler.
This introduces significant dispatch latency, with overhead that makes the process worthwhile only when the application requires a very large amount of parallel computation. Further, if a program running on the GPU wants to directly generate work-items, either for itself or for the CPU, it is impossible today! HSA creates an improved processor design that exposes the benefits and capabilities of mainstream programmable compute elements, working together seamlessly.
To fully exploit the capabilities of parallel execution units, it is essential for computer system designers to think differently. The designers must re-architect computer systems to tightly integrate the disparate compute elements on a platform into an evolved central processor while providing a programming path that does not require fundamental changes for software developers.
This is the primary goal of the new HSA design.