Research On Heterogeneous Parallel Techniques For Power System Steady-State Analysis Application

Power system analyses are an essential part of electrical power system planning,design and dispatch control.Many experiments and research can be conducted based on power system analysis method.The rapid development of electric power systems with the advancements made in new technologies and new devices has brought about unseen complexity to power system analysis.Parallel computing became one of promising methods to improve power system analysis efficiency.Graphics Processing Units(GPUs)are now ubiquitous and provide unprecedented computational power for scientific applications.How to boost up the powerful GPU parallel processing capability to solve power system analysis problem has become a hot research topic.However,the architecture complexity of the hybrid system poses many challenges to the parallel algorithms designed on the infrastructure.First,it must exploit multi-level parallelism to improve the efficiency of programs on CPU-GPU node,or it will degrade program performance,and hardware resources aren’t fully utilized.Secondly,current programing model is substantially difficult to use on massive GPU cluster.It is necessary to provide unified and easy-to-use programming model on the heterogeneous parallel computing systems for programmers.To address the above challenges,this thesis discusses key technologies of heterogeneous parallel computing based on power system steady-state analysis applications which include power flow analysis,static state security analysis and static security checking of dispatching plans.Newton-Raphson method is a common power flow solver.The heavy computational part of the method is to solve the sparse linear equations.The time-consuming part of large sets of linear equations is often the LU factorization of sparse,symmetric Jacobian matrix.The performance of sparse Jacobian matrix decomposition decreases due to irregular compute and memory access pattern.This thesis introduces a parallel multifrontal solver algorithm on GPU.Parallel multifrontal sparse matrix factorization method turns the sparse problem into a tree of dense matrix operations which are suitable for massively parallel multi-core CPUs and GPUs.This thesis proposes a hybrid CPU-GPU task allocation model and task scheduling policy.Many Task Execution Units(TEUs)are created and can execute multiple GEMM tasks in parallel in a kernel of GPU.And some important optimization methods such as the multi-level memory access patterns,the overlap of communication with computation are used.The new parallel power flow solver on hybrid CPU-GPU system fully exploits all available compute resources of a heterogeneous system and achieves a speedup of 3.95 on MATPOWER case24906 when compared to a multifrontal multithreaded power flow solver on CPU.Contingency analysis method is in common use for power system static security assessment application.Contingency analysis involves low-rank matrix-matrix multiplication,low-rank matrix inversion and low-rank matrix-vector multiplication.Current algorithms for low-rank matrix operations often suffer from poor GPU utilization.This thesis proposes a novel low-rank matrix-matrix combined multiplication algorithm,a novel low-rank matrix inversion algorithm by partitioning,and a novel low-rank matrix-vector combined multiplication algorithm.The GPU-optimized data structures and scheduling policy for threads are designed to improve load balancing and exploit thread level parallelism on GPU.Data reuse,access coalescing to global memory,eliminating share-memory bank conflict and some other memory access optimization techniques are used to effectively improve the efficiency of data access and occupancy rate of GPUs.So that the new static security assessment algorithm gets a speedup of 1.8～1.9 on hybrid CPU-GPU platforms compared to CPU on MATPOWER case6240.Static Security Checking(SSC)of large grid’s dispatching plans aims to assess the system steady state performance for all possible postulated contingencies.It is different from the above-mentioned algorithms of power flow calculation and static security assessment on one hybrid CPU-GPU node.SSC applications run on the GPU clusters.Both shared-memory and message-passing architectures are the conventional shared-address space programming models on large-scale,distributed-memory multiprocessors.Those programming models are difficult to use on massive GPU cluster for programmers.We argue that the transparent,global-view access to arrays data distributed across the memories of a distributed system is a better choice for programming on a heterogeneous system.This thesis implements a library based on PGAS programming model on GPU cluster –CUDA-GA,which extends GA library for a hybrid CPU-GPU system.It extends Com Ex/ARMCI library with reliable UDP messaging,and builds a distributed-executor framework on GPU cluster.A novel static security check algorithm of large grid’s dispatching plans has been developed with CUDA-GA and saves 20%～30% calculation time on GPU compared to MPI+CUDA.In summary,this thesis studies multifrontal LU factorization of sparse symmetric Jacobian matrix,low-rank matrix operations,heterogeneous PGAS programming model on CPU-GPU platform and implements power system steady-state analysis algorithms on hybrid CPU-GPU system.These technologies greatly improve the performance of the power system steady-state analysis applications and are of great significance for keeping the power grid stable.