Calculation of Surface Offset Gathers Based on Reverse Time Migration and Its Parallel Computation with Multi-GPUs

As an important method for seismic data processing, reverse time migration (RTM) has high precision but involves high-intensity calculations. The calculation an RTM surface offset (shot–receiver distance) domain gathers provides intermediary data for an iterative calculation of migration and its velocity building. How to generate such data efficiently is of great significance to the industrial application of RTM. We propose a method for the calculation of surface offset gathers (SOGs) based on attribute migration, wherein, using migration calculations performed twice, the attribute profile of the surface offsets can be obtained, thus the image results can be sorted into offset gathers. Aiming at the problem of high-intensity computations required for RTM, we put forth a multi-graphic processing unit (GPU) calculative strategy, i.e., by distributing image computational domains to different GPUs for computation and by using the method of multi-stream calculations to conceal data transmission between GPUs. Ultimately, the computing original efficiency was higher relative to a single GPU, and more GPUs were used linearly. The test with a model showed that the attributive migration methods can correctly output SOGs, while the GPU parallel computation can effectively improve the computing efficiency. Therefore, it is of practical importance for this method to be expanded and applied in industries.

Hierarchical Multi-Blockchain System for Parallel Computation in Cryptocurrency Transfers and Smart Contracts

Most of the existing smart-contract-based cryptocurrencies, such as Ethereum, use an account-based ledger. However, while the account-based model is advantageous for the efficient use of smart contracts and the increased exchangeability of cryptocurrencies, it is not well-suited to the parallel execution of smart contracts. However, unspent transaction output (UTXO)-based cryptocurrencies such as Bitcoin are advantageous for parallel cryptocurrency transfers but not well-suited to smart contracts. In this paper, we propose a hierarchical multi-blockchain system that uses multiple pairs of sidechain and dual-sidechains extended by independent block mining in their blockchain networks and a mainchain to control the branching and connection process of sidechains and dual sidechains. In the proposed method, one pair of a sidechain and dual sidechain forms one shard. The proposed method uses multiple shards to execute cryptocurrency transfers and smart contracts in parallel. In addition, the proposed model uses an accoutchain to record the resulting state changes generated by smart contract executions in each shard and securely share them with all other nodes. The proposed method uses a modifiable blockchain structure for the accountchain to obtain the database to record the smart contract execution results in each shard in as small and secure a manner as possible to ensure that all nodes trust the recorded results without executing smart contracts themselves. To examine the validity of the proposed method, we conducted a threat analysis of the proposed method by examining possible attacks in various scenarios as a thought experiment. This threat analysis concludes that the proposed blockchain system can execute smart contracts in parallel while keeping the concurrency in resulting state changes secure.

Massively Parallel Computation via Remote Memory Access

We introduce the Adaptive Massively Parallel Computation (AMPC) model, which is an extension of the Massively Parallel Computation (MPC) model. At a high level, the AMPC model strengthens the MPC model by storing all messages sent within a round in a distributed data store. In the following round, all machines are provided with random read access to the data store, subject to the same constraints on the total amount of communication as in the MPC model. Our model is inspired by the previous empirical studies of distributed graph algorithms [8, 30] using MapReduce and a distributed hash table service [17]. This extension allows us to give new graph algorithms with much lower round complexities compared to the best-known solutions in the MPC model. In particular, in the AMPC model we show how to solve maximal independent set in O (1) rounds and connectivity/minimum spanning tree in O (log log m / n n rounds both using O ( n δ ) space per machine for constant δ < 1. In the same memory regime for MPC, the best-known algorithms for these problems require poly log n rounds. Our results imply that the 2-C YCLE conjecture, which is widely believed to hold in the MPC model, does not hold in the AMPC model.

Comparison of Serial and Parallel Computation on Predicting Missing Data with EM Algorithm

One way to deal with the presence of missing value or incomplete data is to impute the data using EM Algorithm. The need for large and fast data processing is necessary to implement parallel computing on EM algorithm serial program. In the parallel program architecture of EM Algorithm in this study, the controller is only related to the EM module whereas the EM module itself uses matrix and vector modules intensively. Parallelization is done by using OpenMP in EM modules which results in faster compute time on parallel programs than serial programs. Parallel computing with a thread of 4 (four) increases speed up, reduces compute time, and reduces efficiency when compared to parallel computing by the number of threads 2 (two).

Parallelization of a 3-Dimensional Hydrodynamics Model Using a Hybrid Method with MPI and OpenMP

Process-based numerical models developed to perform hydraulic/hydrologic/water quality analysis of watersheds and rivers have become highly sophisticated, with a corresponding increase in their computation time. However, for incidents such as water pollution, rapid analysis and decision-making are critical. This paper proposes an optimized parallelization scheme to reduce the computation time of the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model, which has been continuously developed for water pollution or algal bloom prediction in rivers. An existing source code and a parallel computational code with open multi-processing (OpenMP) and a message passing interface (MPI) were optimized, and their computation times compared. Subsequently, the simulation results for the existing EFDC model and the model with the parallel computation code were compared. Furthermore, the optimal parallel combination for hybrid parallel computation was evaluated by comparing the simulation time based on the number of cores and threads. When code parallelization was applied, the performance improved by a factor of approximately five compared to the existing source code. Thus, if the parallel computational source code applied in this study is used, urgent decision-making will be easier for events such as water pollution incidents.