Automated Derivation of Parametric Data Movement Lower Bounds for Affine Programs

Abstract

For most relevant computation, the energy and time needed for data movement dominates that for performing arithmetic operations on all computing systems today. Hence it is of critical importance to understand the minimal total data movement achievable during the execution of an algorithm. The achieved total data movement for different schedules of an algorithm can vary widely depending on how efficiently the cache is used, e.g., untiled versus effectively tiled matrix-matrix multiplication. A significant current challenge is that no existing tool is able to meaningfully quantify the potential reduction to the data movement of a computation that can be achieved by more effective use of the cache through operation rescheduling. Asymptotic parametric expressions of data movement lower bounds have previously been manually derived for a limited number of algorithms, often without scaling constants. In this paper, we present the first compile-time approach for deriving non-asymptotic parametric expressions of data movement lower bounds for arbitrary affine computations.The approach has been implemented in a fully automatic tool (IOLB) that can generate these lower bounds for input affine programs. IOLB's use is demonstrated by exercising it on all the benchmarks of the PolyBench suite. The advantages of IOLB are many: (1) IOLB enables us to derive bounds for few dozens of algorithms for which these lower bounds have never been derived. This reflects an increase of productivity by automation.(2) Anyone is able to obtain these lower bounds through IOLB, no expertise is required. (3) For some of the most well-studied algorithms, the lower bounds obtained by IOLB are higher than any previously reported manually derived lower bounds.

Compiling Neural Networks for a Computational Memory Accelerator

“…Polyhedral compilation Polyhedral compilation enables reasoning about properties of code in the form of nested loops with affine bounds, and have been used for many applications including optimizations [44,45], accelerator mapping [46], program verification [47], modeling caches [48,49], and computing bounds on IO complexity [50]. Indeed, many compiler frameworks that target NN workloads utilize polyhedral compilation techniques [51][52][53][54].…”