Large-scale multithreading requires execution contexts to be compact in order to fit many contexts within on-chip memories. The number of thread contexts supported by a GPU core is shader-program dependent and typically limited by the size of on-chip storage. GPUs require compiled shader binaries to declare input and output entity sizes, as well as bounds on temporary storage and scratch registers required for execution. At runtime, GPUs use these bounds to partition unspillable on-chip storage (including data registers) dynamically among execution contexts. Thus, GPUs support many thread contexts (up to an architecture-specific bound) and, correspondingly, provide maximal latency-hiding ability when shaders use fewer resources. When shaders require large amounts of storage, the number of execution contexts provided by a GPU drops. (The accompanying sidebar details an example of the efficient execution of a fragment shader on a GPU core.)
A GPU’s programmable cores interoperate with a collection of specialized fixed-function processing units that provide high-performance, power-efficient implementations of nonshader stages. These components do not simply augment programmable processing; they perform sophisticated operations and constitute an additional hundreds of gigaflops of processing power. Two of the most important operations performed via fixed-function hardware are texture filtering and rasterization (fragment generation).
Texturing is handled almost entirely by fixed-function logic. A texturing operation samples a contiguous 1D, 2D, or 3D signal (a texture) that is discretely represented by a multidimensional array of color values (2D texture data is simply an image). A GPU texture-filtering unit accepts a point within the texture’s parameterization (represented by a floating-point tuple, such as {. 5,.75}) and loads array values surrounding the coordinate from memory. The values are then filtered to yield a single result that represents the texture’s value at the specified coordinate. This value is returned to the calling shader function. Sophisticated texture filtering is required for generating high-quality images. As graphics APIs provide a finite set of filtering kernels, and because filtering kernels are computationally expensive, texture filtering is well suited for fixed-function processing.
Primitive rasterization in the FG stage is another key pipeline operation implemented by fixed-function components. Rasterization involves densely sampling a primitive (at least once per output image pixel) to determine
which pixels the primitive overlaps. This process involves interpolating the location of the surface at each sample point and then generating fragments for all sample points covered by the primitive. Bounding-box computations and hierarchical techniques optimize the rasterization process. Nonetheless, rasterization involves significant computation.
In addition to the components for texturing and rasterization, GPUs contain dedicated hardware components for operations such as surface visibility determination, output pixel compositing, and data compression/decom-pression.
Parallel-processing resources place extreme load on a GPU’s memory system, which services memory requests from both fixed-function and programmable compo-
nents. These requests include a mixture of fine-granularity and bulk prefetch operations and may even require realtime guarantees (such as display scan out).
Recall that a GPU’s programmable cores tolerate large memory latencies via hardware multithreading and that interstage stream data accesses can be prefetched. As a result, GPU memory systems are architected to deliver high-bandwidth, rather than low-latency, data access. High throughput is obtained through the use of wide
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