The Indonesia Colorectal Cancer Consortium (IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the MapReduce and Compute Unified Device Architecture (CUDA) frameworks and evaluate performance (speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data (1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620 (6 cores) and NVIDIA Tesla K20 over sequential processing.