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Each multiplication, addition, or subtraction is operating on a scalar value instead of a four-vector, wasting computational resources. While the second line of this snippet (the actual texture lookup) is already as concise as possible, the first line leaves a lot to be desired. From them, a new coordinate offset is computed that will be used to look up values from the texture Operator: float2 offset = float2(params.x * center.x - 0.5f * (params.x - 1.0f), params.x * center.y - 0.5f * (params.x - 1.0f)) float4 O = f4texRECT(Operator, offset)
Graphic card benchmark manipulation code#
In the following code snippet, we have a texture coordinate center and a uniform value called params. Let's take an example from a real-world application written by a first-time GPU programmer. As a result, it's not uncommon to see new GPU programmers writing code that ineffectively utilizes vector arithmetic. Some count on the compiler to make use of SIMD extensions when possible some ignore the extensions entirely. While modern CPUs do have SIMD processing extensions such as MMX or SSE, most CPU programmers never attempt to use these capabilities themselves. (See Chapter 44 of this book, "A GPU Framework for Solving Systems of Linear Equations," for examples of this idea in practice.) 35.1.1 Instruction-Level Parallelism Packing the data in such a way that multiple identical scalar operations can occur simultaneously provides another means of exploiting the inherent parallelism of the GPU. For example, operations on a large array of scalar data will be inherently scalar. Furthermore, parallelism can often be extracted by rearranging the data itself.
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For example, a series of sequential but independent scalar multiplications might be combined into a single four-component vector multiplication. This provides ample opportunities for the extraction of instruction-level parallelism within a GPU program. They can schedule more than one of these instructions per cycle per pipeline. Vertex and fragment processors operate on four-vectors, performing four-component instructions such as additions, multiplications, multiply-accumulates, or dot products in a single cycle. This parallelism exists at several levels on the GPU, as described in Chapter 29 of this book, "Streaming Architectures and Technology Trends." First, parallel execution on multiple data elements is a key design feature of modern GPUs.
Graphic card benchmark manipulation how to#
One of the biggest hurdles you'll face when first programming a GPU is learning how to get the most out of a data-parallel computing environment. The goal of this chapter is to help CPU programmers who are new to GPU programming avoid some of these common mistakes so that they gain the benefits of GPU performance without all the headaches of the GPU programmer's learning curve. But an interesting trend has appeared along the way: it seems that many programmers make the same performance mistakes in their GPU programs regardless of how much experience they have programming CPUs.
The CD content, including demos and content, is available on the web and for download.Īs GPU programmability has become more pervasive and GPU performance has become almost irresistibly appealing, increasing numbers of programmers have begun to recast applications of all sorts to make use of GPUs. You can purchase a beautifully printed version of this book, and others in the series, at a 30% discount courtesy of InformIT and Addison-Wesley. GPU Gems 2 GPU Gems 2 is now available, right here, online.
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