Storage assignment optimizations to generate compact and efficient code on embedded DSPs

• Authors: Amit Rao;Santosh Pande
• Venue: Proceedings of the ACM SIGPLAN 1999 conference on Programming language design and implementation
• Year: 1999

DSP architectures typically provide dedicated memory address generation units and indirect addressing modes with auto-increment and auto-decrement that subsume address arithmetic calculation. The heavy use of auto-increment and auto-decrement indirect addressing require DSP compilers to perform a careful placement of variables in storage to minimize address arithmetic instructions to generate compact and efficient DSP code. Liao et al. formulated the problem of storage assignment as the simple offset assignment problem (SOA) and the general offset assignment problem (GOA), and proposed heuristic solutions.The storage allocation of variables critically depends on the sequence of variable accesses. In this paper we present techniques to optimize the access sequence of variables by applying algebraic transformations (such as commutativity and associativity) on expression trees to obtain the least cost offset assignment. We develop a new formulation of this problem as the least cost access sequence problem (LCAS). Based on the proposed framework, we develop heuristic algorithms that determine empirically near-optimal solutions resulting in fewer address arithmetic instructions. We have implemented the proposed heuristic algorithms by extending the storage assignment optimization in the SPAM compiler back-end targeted for the TMS320C25 DSP. In the case of SOA, experimental results for programs from the DSPstone benchmark suite show an average improvement of 3.36% in static code size and an average relative speed-up of 7.28% over results obtained using existing SOA algorithms. The average code size reduction over code compiled with a naive storage assignment algorithm is 7.04%. The proposed framework has also been applied to the GOA problem and shows average code size reductions of 2.04% over results obtained using existing GOA algorithms, and average code size reductions of 10.84% over a naive GOA algorithm. Code size reduction and improvement in dynamic instruction counts could be valuable given limited memory and real-time response requirements placed on embedded systems.