The compute work rasterizer or the GigaThread Engine of a modern NVIDIA GPU focuses on maximizing compute work occupancy across all streaming multiprocessors in a GPU while retaining design simplicity. In this article, we identify the operational aspects of the GigaThread Engine that help it meet those goals but also lead to less-than-ideal cache locality for texture accesses in 2D compute shaders, which are an important optimization target for gaming applications. We develop three software techniques, namely LargeCTAs , Swizzle , and Agents , to show that it is possible to effectively exploit the texture data working set overlap intrinsic to 2D compute shaders. We evaluate these techniques on gaming applications across two generations of NVIDIA GPUs, RTX 2080 and RTX 3080, and find that they are effective on both GPUs. We find that the bandwidth savings from all our software techniques on RTX 2080 is much higher than the bandwidth savings on baseline execution from inter-generational cache capacity increase going from RTX 2080 to RTX 3080. Our best-performing technique, Agents , records up to a 4.7% average full-frame speedup by reducing bandwidth demand of targeted shaders at the L1-L2 and L2-DRAM interfaces by 23% and 32%, respectively, on the latest generation RTX 3080. These results acutely highlight the sensitivity of cache locality to compute work rasterization order and the importance of locality-aware cooperative thread array scheduling for gaming applications.

In this article, we first characterize register operand value locality in shader programs of modern gaming applications and observe that there is a high likelihood of one of the register operands of several multiply, logical-and, and similar operations being zero, dynamically. We provide intuition, examples, and a quantitative characterization for how zeros originate dynamically in these programs. Next, we show that this dynamic behavior can be gainfully exploited with a profile-guided code optimization called Zeroploit that transforms targeted code regions into a zero-(value-)specialized fast path and a default slow path. The fast path benefits from zero-specialization in two ways, namely: (a) the backward slice of the other operand of a given multiply or logical-and can be skipped dynamically, provided the only use of that other operand is in the given instruction, and (b) the forward slice of instructions originating at the given instruction can be zerospecialized, potentially triggering further backward slice specializations from operations of that forward slice as well. Such specialization helps the fast path avoid redundant dynamic computations as well as memory fetches, while the fast-slow versioning transform helps preserve functional correctness. With an offline value profiler and manually optimized shader programs, we demonstrate that Zeroploit is able to achieve an average speedup of 35.8% for targeted shader programs, amounting to an average frame-rate speedup of 2.8% across a collection of modern gaming applications on an NVIDIA® GeForce RTX TM 2080 GPU.