2016/03/08 by Ning Hao, Amirreza Oghbaee, Hao, Ning +8
Computer Science · Engineering · Mathematics · #Distributed #Error Correcting Code Techniques #FOS: Computer and information sciences #FOS: Mathematics #Ferroelectric and Negative Capacitance Devices #Mathematical Software (cs.MS) #Optimization and Control (math.OC) #Parallel #VLSI and FPGA Design Techniques #and Cluster Computing (cs.DC) #cs.DC #cs.MS #math.OC
paper · pdf · doi:10.48550/arxiv.1603.02526
arxiv created 2016/03/08 · openalex publication_date 2016/03/08 · arxiv updated 2016/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
There is an ongoing effort to develop tools that apply distributed computational resources to tackle large problems or reduce the time to solve them. In this context, the Alternating Direction Method of Multipliers (ADMM) arises as a method that can exploit distributed resources like the dual ascent method and has the robustness and improved convergence of the augmented Lagrangian method. Traditional approaches to accelerate the ADMM using multiple cores are problem-specific and often require multi-core programming. By contrast, we propose a problem-independent scheme of accelerating the ADMM that does not require the user to write any parallel code. We show that this scheme, an interpretation of the ADMM as a message-passing algorithm on a factor-graph, can automatically exploit fine-grained parallelism both in GPUs and shared-memory multi-core computers and achieves significant speedup in such diverse application domains as combinatorial optimization, machine learning, and optimal control. Specifically, we obtain 10-18x speedup using a GPU, and 5-9x using multiple CPU cores, over a serial, optimized C-version of the ADMM, which is similar to the typical speedup reported for existing GPU-accelerated libraries, including cuFFT (19x), cuBLAS (17x), and cuRAND (8x).