Mille Cheval: a GPU-based in-memory high-performance computing framework for accelerated processing of big-data streams

Streams are temporally ordered, rapid changing, ample in volume, and infinite in nature. It is nearly impossible to store the entire data stream due to its large volume and high velocity. In this work, the principle of parallelism is employed to accelerate stream data computing. GPU-based high-performance computing (HPC) framework is proposed for accelerated processing of big-data streams using the in-memory data structure. We have implemented three parallel algorithms to prove the viability of the framework. The contributions of Mille Cheval are: (1) the viability of streaming on accelerators to increase throughput, (2) carefully chosen hash algorithms to achieve low collision rate and high randomness, and (3) memory sketches for approximation. The objective is to leverage the power of a single node using in-memory computing and hybrid computing. HPC does not always require high-end hardware but well-designed algorithms. Achievements of Mille Cheval are: (1) relative error is 1.32 when error rate and overestimate rate are chosen as 0.001 and (2) the host memory space requirement is just 63 MB for 1 terabyte of data. The proposed algorithms are pragmatic. It is evident from experimental results that the framework demonstrates 10X speed-up as compared with CPU implementations and 3X speed-up as compared with GPU implementations.