Abstract-The rapid pace of change in 3D game technology makes workload characterization necessary for every game generation. Comparing to CPU characterization, far less quantitative information about games is available. This paper focuses on analyzing a set of modern 3D games at the API call level and at the microarchitectural level using the Attila simulator. In addition to common geometry metrics and, in order to understand tradeoffs in modern GPUs, the microarchitectural level metrics allow us to analyze performance key characteristics such as the balance between texture and ALU instructions in fragment programs, dynamic anisotropic ratios, vertex, z-stencil, color and texture cache performance. I.INTRODUCTIONGPU design and 3D game technology evolve side by side in leaps and bounds. Game developers deploy computationally demanding 3D effects that use complex shader programs with multiple texture accesses that are highly tuned to extract the maximum performance on the existing and soon-to-bereleased GPUs. On the other hand, GPU designers carefully tailor and evolve their designs to cater for the needs of the next generation games expected to be available when the GPU launches. To this end, they put on the market high-end and middle-end graphics cards with substantial vertex/pixel processing enhancements w.r.t. previous generations, expecting to achieve high frame rates in newly released games.As with any other microprocessor design, carefully understanding, characterizing and modeling the workloads at which a given GPU is aimed, is key to predict and meet its performance targets. There is extensive literature characterizing multiple CPU workloads [24][25] [26]. Compared to the CPU world, though, there is a lack of published data on 3D workloads in general, and interactive games in particular. The reasons are manifold: GPUs still have a wide variety of fixed functions that are difficult to characterize and model (texture sampling, for example), GPUs are evolving very fast, with continuous changes in their programming model (from fixed geometry and fixed texture combiners to full-fledged vertex and fragment programs), the games are also rapidly evolving to exploit these new programming models, and new functions are constantly added to the rendering pipeline as higher silicon densities makes the on-die integration of these functions cost-effective (geometry shaders and tessellation, for example). All these reasons combine to produce an accelerated rate of change in the workloads and even relatively recent studies rapidly obsolete.For example, [1][2] characterize the span processing workload in the rasterization stage. However, today all GPUs use the linear edge function rasterization algorithm [6], making span processing no longer relevant. As another example, [1] studies the geometry BW requirements per frame, which has nowadays substantially decreased thanks to the use of indexed modes and storing vertexes in local GPU memory.The goal of this work is to analyze and characterize a set of recent OpenGL (OGL) and ...
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