In this paper, we propose and evaluate a simple mechanism to accelerate iterative machine learning algorithms implemented in Hadoop map-reduce (stock), and Apache Spark. In particular, we describe a technique that enables data parallel tasks in map-reduce and Spark to be dynamically and adaptively scheduled on CPU or GPU, based on availability and load. We examine the extent of performance improvements, and correlate them to various parameters of the algorithms studied. We focus on end-to-end performance impact, including overheads associated with transferring data into and out of the GPU, and conversion between data representations in the JVM and on GPU. We also present three optimizations that, in our analysis, can be generalized across many iterative machine learning applications. We present a case study where we accelerate four iterative machine learning applications-multinomial logistic regression, multiple linear regression, K-Means clustering and principal components analysis using singular value decomposition, implemented in three data analytics frameworks-Hadoop Map-Reduce (HMR), IBM Main-Memory Map-Reduce (M3R) and Spark. We observe that the use of GPGPUs decreases the execution time of these applications on HMR by up to 8X, M3R by up to 18X, and Spark by up to 25X. Through our empirical analysis, we offer several insights that can be helpful in designing middleware and cluster managers to accelerate map-reduce and Spark applications using GPUs.