Towards heterogeneous solvers for large-scale linear systems

Applying Linear Regression to systems with a massive amount of observations, a scenario which is becoming increasingly common in the era of Big Data, poses major algorithmic and computational challenges. This paper proposes a novel high-performance FPGA-based architecture for large-scale Linear Regression problems as well as a heterogeneous system comprising the custom FPGA architecture, an enhanced GPU module and a multi-core CPU for addressing the aforementioned problem. The system adaptively assigns Linear Regression workloads to the three computing devices to minimise runtime. The device with the highest performance is chosen based on an analytical framework, as well as the workload´s size and structure. A quantitative comparison with existing FPGA, GPU and multi-core CPU designs yields speed-ups of up to 18.07×, 32.67× and 25.84× respectively.