DTM-NUCA: dynamic texture mapping-NUCA for energy-efficient graphics rendering

Modern mobile GPUs integrate an increasing number of shader cores to speedup the execution of graphics workloads. Each core integrates a private Texture Cache to apply texturing effects on objects, which is backed-up by a shared L2 cache. However, as in any other memory hierarchy, such organization produces data replication in the upper levels (i.e., the private Texture Caches) to allow for faster accesses at the expense of reducing their overall effective capacity. E.g., in a mobile GPU with four shader cores, about 84.6% of the requested texture blocks are replicated in at least one of the other private Texture Caches. This paper proposes a novel dynamically-mapped NonUniform Cache Architecture (NUCA) organization for the private Texture Caches of a mobile GPU aimed at increasing their effective overall capacity and decreasing the overall access latency by attacking data replication. A block missing in a local Texture Cache may be serviced by a remote one at a cost smaller than a round trip to the shared L2. The proposed Dynamic Texture Mapping-NUCA (DTM-NUCA) features a lightweight mapping table, called Affinity Table, that is independent of the L2 cache size, unlike a traditional NUCA organization. The best owner for a given set of blocks is dynamically determined and stored in the Affinity Table to maximize local accesses. The mechanism also allows for a certain amount of replication to favor local accesses where appropriate, without hurting performance due to the small capacity loss resulting from the allowed replication. DTM-NUCA is presented in two flavors. One with a centralized Affinity Table, and another with a distributed Affinity Table. Experimental results show first that the L2 pressure is effectively reduced, eliminating 41.8% of the L2 accesses on average. As for the average latency, DTM-NUCA performs a very effective job at maximizing local over remote accesses, achieving 73.8% of local accesses on average. As a consequence, our novel DTM-NUCA organization obtains an average speedup of 16.9% and overall 7.6% energy savings over a conventional organization.This work has been supported by the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency (MCIN/AEI) under grant PID2020-113172RB-I00, the ICREA Academia program and a research fellowship from the University of Murcia’s “Plan Propio de Investigacion”.Peer ReviewedPostprint (author's final draft