Grex: An efficient MapReduce framework for graphics processing units

In this paper, we present a new MapReduce framework, called Grex, designed to leverage general purpose graphics processing units (GPUs) for parallel data processing. Grex provides several new features. First, it supports a parallel split method to tokenize input data of variable sizes, such as words in e-books or URLs in web documents, in parallel using GPU threads. Second, Grex evenly distributes data to map/reduce tasks to avoid data partitioning skews. In addition, Grex provides a new memory management scheme to enhance the performance by exploiting the GPU memory hierarchy. Notably, all these capabilities are supported via careful system design without requiring any locks or atomic operations for thread synchronization. The experimental results show that our system is up to 12.4× and 4.1× faster than two state-of-the-art GPU-based MapReduce frameworks for the tested applications.

► Parallel input splitting in GPU instead of partitioning data at the host CPU.
► Even distribution of intermediate pairs to GPU threads.
► Efficient use of GPU memory transparently utilizing shared and texture caches.
► Delayed generation of value part of a pair replaces global writes with cache emits.