DejaVu: Unifying Memory Allocations to Eliminate Redundant Copies on Unified-Memory SoCs

Abstract

GPU applications on unified-memory (UMA) edge platforms often inherit a discrete-GPU memory abstraction in which they allocate one buffer for the CPU, another for the GPU, and copy data between them before and after GPU execution. On UMA hardware these buffers reside in the same physical DRAM, so the copies consume bandwidth, time, and energy without moving data across a physical boundary. Despite the growing adoption of UMA platforms, this pattern remains common because production software stacks, libraries, and samples were written for portability across discrete GPUs. However, removing these copies is not as simple as merging the two buffers, because the original program may rely on the two buffers being distinct, or on the copy itself ordering CPU and GPU accesses. DejaVu removes these copies only when the program does not depend on the effects above. It does so along two complementary paths, depending on whether source is available. DejaVu-SR is a compile-time LLVM transformation that proves safety and rewrites accepted pairs in place. DejaVu-DR is a profile-guided binary optimizer for closed-source deployments that profiles and validates stable allocation/copy patterns and, at runtime, intercepts the matching calls to coalesce profile-matched pairs while preserving the ordering effects of removed copies. Across seven benchmarks on three NVIDIA Jetson platforms, DejaVu's benefit grows with the fraction of baseline time spent on copies. Copy-dominated workloads speed up by up to 6.9$\times$, closed-source end-to-end applications speed up by 1.10-1.14$\times$. Both source and binary paths achieve $\ge$99% of the performance achievable by manual optimization.