Juan Luis Aragón | Searcharxiv

arXiv2026

Design of a GPU with Heterogeneous Cores for Graphics

Heterogeneous architectures can deliver higher performance and energy efficiency than symmetric counterparts by using multiple architectures tuned to different types of workloads. While previous works focused on CPUs, this work extends the concept of heterogeneity to GPUs by proposing KHEPRI, a heterogeneous GPU architecture for graphics applications. Scenes in graphics applications showcase diversity, as they consist of many objects with varying levels of complexity. As a result, computational intensity and memory bandwidth requirements differ significantly across different regions of each scene. To address this variability, our proposal includes two types of cores: cores optimized for high ILP (compute-specialized) and cores that tolerate a higher number of simultaneously outstanding cache misses (memory-specialized). A key component of the proposed architecture is a novel work scheduler that dynamically assigns each part of a frame (i.e., a tile) to the most suitable core. Designing this scheduler is particularly challenging, as it must preserve data locality; otherwise, the benefits of heterogeneity may be offset by the penalty of additional cache misses. Additionally, the scheduler requires knowledge of each tile's characteristics before rendering it. For this purpose, KHEPRI leverages frame-to-frame coherence to predict the behavior of each tile based on that of the corresponding tile in the previous frame. Evaluations across a wide range of commercial animated graphics applications show that, compared to a traditional homogeneous GPU, KHEPRI achieves an average performance improvement of 9.2%, a throughput increase (frames per second) of 7.3%, and a total GPU energy reduction of 4.8%. Importantly, these benefits are achieved without any hardware overhead.

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arXiv2024

WaSP: Warp Scheduling to Mimic Prefetching in Graphics Workloads

Contemporary GPUs are designed to handle long-latency operations effectively; however, challenges such as core occupancy (number of warps in a core) and pipeline width can impede their latency management. This is particularly evident in Tile-Based Rendering (TBR) GPUs, where core occupancy remains low for extended durations. To address this challenge, we introduce WaSP, a lightweight warp scheduler tailored for GPUs in graphics applications. WaSP strategically mimics prefetching by initiating a select subset of warps, termed priority warps, early in execution to reduce memory latency for subsequent warps. This optimization taps into the inherent but underutilized memory parallelism within the GPU core. This underutilization is a consequence of a baseline scheduler that evenly spaces misses throughout execution to exploit the inherent spatial locality in graphics workloads. WaSP improves on this by reducing average memory latency while maintaining locality for the majority of warps. While maximizing memory parallelism utilization, WaSP prevents saturating the caches with misses to avoid filling up the MSHRs (Miss Status Holding Registers). This approach reduces cache stalls that halt further accesses to the cache. Overall, WaSP yields a significant 3.9% performance speedup. Importantly, WaSP accomplishes these enhancements with a negligible overhead, positioning it as a promising solution for enhancing the efficiency of GPUs in managing latency challenges.

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