V-PFORDelta: Data Compression for Energy Efficient Computation of Time Series

Abstract

Chip multiprocessors (CMPs) and heterogeneous architectures have become predominant in all market segments, from embedded to high performance computing. These architectures exacerbate on-chip data requirements, creating additional pressure on the memory subsystem. Consequently, efficient utilization of on-chip memory space becomes critical for data intensive applications. A promising means of addressing this challenge is to use an effective compression method to reduce the data transmitted along the memory hierarchy. In this paper we present V-PFORDelta, a real-time vectorized integer differential compression method for memory bound applications. We evaluate the effectiveness of our SIMD (Single Instruction Multiple Data stream) based compression method on an industrial hydrological time series data processing kernel. We analyzed both Streaming SIMD Extensions (SSE) and Advanced Vector Extensions 2 (AVX2) versions of the compression method. Results show that the performance and energy efficiency can be improved up to a factor of 3.1 and 8.2, respectively. The proposed method not only outperforms the uncompressed SIMD implementations of the hydrological kernel, but also reduces the data storage requirements by a factor of 1.56x to 3.38x, depending on the analyzed dataset.