The SEPO Model of Computation to Enable Larger-Than-Memory Hash Tables for GPU-Accelerated Big Data Analytics

The massive parallelism and high memory bandwidth of GPU's are particularly well matched with the exigencies of Big Data analytics applications, for which many independent computations and high data throughput are prevalent. These applications often produce (intermediary or final) results in the form of key-value (KV) pairs, and hash tables are particularly well-suited for storing these KV pairs in memory. How such hash tables are implemented on GPUs, however, has a large impact on performance. Unfortunately, all hash table solutions designed for GPUs to date have limitations that prevent acceleration for Big Data analytics applications. In this paper, we present the design and implementation of a GPU-based hash table for efficiently storing the KV pairs of Big Data analytics applications. The hash table is able to grow beyond the size of available GPU memory without excessive performance penalties. Central to our hash table design is the SEPO model of computation, where the processing of individual tasks is selectively postponed when processing is expected to be inefficient. A performance evaluation on seven GPU-based Big Data analytics applications, each processing several Gigabytes of input data, shows that our hash table allows the applications to achieve, on average, a speedup of 3.5 over their CPU-based multi-threaded implementations. This gain is realized despite having hash tables that grow up to four times larger than the size of available GPU memory.