An Efficient Reduced-Memory GPU-based Dynamic Programming Strategy for Bounded Knapsack Problems

TL;DR: In this article , an implementation of an efficient dynamic programming technique to solve BKP on GPU-based system via CUDA is presented, which shows brilliant speedup over sequential implementation of the dynamic technique.

Abstract: Many dynamic programming approaches are existing for 1-0 Knapsack problem (KP) for fast GPU-based solution. These dynamic programming methods can be used for solving the problem of Bounded Knapsack Problem (BKP) after converting it into an equivalent 1-0 Knapsack problem. But, after conversion, large problem of BKP becomes too large 1-0 KP to fathoming via these methods on GPU-based system because of limited space availability in GPU device. In this paper, we present an implementation of an efficient dynamic programming technique to solve BKP on GPU-based system via CUDA. With an aspect to preserving the original problem size, BKP is not required to convert into equivalent 1/0 knapsack problem in this approach. Large problems are carried out with only minimally requisite CPU-GPU interactions and less memory occupancy on GPU for better efficiency and memory utilization. This GPU oriented parallel procedure shows brilliant speedup over sequential implementation of the dynamic technique.