Different parallelization techniques for reductions have been proposed elsewhere, that we have classified in this paper into two classes: LPO (Loop Partitioning Oriented techniques) and DPO (Data Partitioning Oriented techniques). We have analyzed both classes in terms of a set of performance properties: data locality, memory overhead, parallelism and workload balancing.In this paper we propose several techniques to increase the exploited parallelism and to introduce load balancing into a DPO method. Regarding parallelism, the solution is based on the partial expansion of the reduction array. For load balance, a first technique is generic, as it can deal with any kind of load unbalancing present in the problem domain.A second technique handles a special case of load unbalancing, appearing when there are a large number of write operations on small regions of the reduction arrays. Efficient implementations of the proposed optimizing solutions for the DWA--LIP DPO method are presented, experimentally tested on static and dynamic kernel codes and compared with other parallel reduction methods.