Heterogeneity in the cell population of cancer tissues poses many challenges in cancer diagnosis and treatment. Studying the heterogeneity in cell populations from gene expression measurement data in the context of cancer research is a problem of paramount importance. In addition, reducing the computation time of the algorithms that deal with high volumes of data has its obvious merits. Parallelizable models using Markov chain Monte Carlo methods are typically slow. This paper shows a novel, computationally efficient, and parallelizable model to analyze heterogeneity in cancer tissues using GPUs. Because our model is parallelizable, the input data size does not affect the computation time much, provided the hardware resources are not exhausted. Our model uses qPCR (quantitative polymerase chain reaction) gene expression measurements to study heterogeneity in cancer tissue. We compute the cell proportion breakup by accelerating variational methods on a GPU. We test this model on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of our algorithm with those of MCMC and Expectation Maximization. Our new model is computationally less complex and faster than existing Bayesian models for cancer tissue heterogeneity.