Affinity Alloc: Taming Not-So Near-Data Computing

To mitigate the data movement bottleneck on large multicore systems, the near-data computing paradigm (NDC) offloads computation to where the data resides on-chip. The benefit of NDC heavily depends on spatial affinity, where all relevant data are in the same location, e.g. same cache bank. However, existing NDC works lack a general and systematic solution: they either ignore the problem and abort NDC when there is no spatial affinity, or rely on error-prone manual data placement. Our insight is that the essential affinity relationship, i.e. data A should be close to data B, is orthogonal to microarchitecture details and input sizes. By co-optimizing the data structure and capturing this general affinity information in the data allocation interface, the allocator can automatically optimize for data affinity and load balance to make NDC computations truly near data. With this insight, we propose affinity alloc, a general framework to optimize data layout for near-data computing. It comprises an extended allocator runtime, co-optimized data structures, and lightweight extensions to the OS and microarchitecture. Evaluated on parallel workloads across broad domains, affinity alloc achieves 2.26x speedup and 1.76x energy efficiency over a state-of-the-art near-data computing technique with 72% traffic reduction.