High-utility itemset mining (HUIM) is an advanced problem of frequent itemset mining, considering the frequency of occurrence and quantitative criteria such as unit profit. Because HUIM can be applied to a broad spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes a high-utility itemset mining task into subtasks to utilize logical parallelism and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in a NUMA-aware manner. Through rigorous and diverse experiments, we found that DPHIM achieved speeds up to 72.7 times faster than the fully tuned serial execution, up to 23.5 times faster than static partitioning, and up to 2.5 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. We also demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.1 to 2.4 times slower on persistent memory.

Abstract Stateless model checking (SMC) is crucial for productivity in verified concurrent programming, and its recent developments for C/C++ and weak memory models are remarkable. The state-of-the-art SMC for C, GenMC, efficiently verifies C programs based on C11 atomics and pthreads. However, it does not support mixed-size accesses, accesses to the same memory region with different-sized types, even though they are ubiquitous in C/C++, particularly the code for memory management. As a result, GenMC does not work for C/C++ programs containing memory management. To resolve this problem, we develop a method of adapting GenMC to mixed-size accesses preserving its optimality. We experimentally evaluate the efficiency of our extended implementation of GenMC and its efficacy for memory management programs.

High-utility itemset mining is a generalized problem of well-known frequent itemset mining, which considers not only the frequency of occurrence but also quantitative criteria such as unit profit. Because it can be applied to a wider spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes the execution of high-utility itemset mining into subtasks in order to leverage logical data parallelism, and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in the NUMA-aware manner. Our intensive and extensive experiments have confirmed that DPHIM performs up to 65.23 times faster than the fully-tuned serial execution, up to 23.54 times faster than static partitioning, and up to 2.51 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. As well, we have demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.07 to 2.43 times slower on persistent memory.

High-utility itemset mining is a generalized problem of well-known frequent itemset mining, which considers not only the frequency of occurrence but also quantitative criteria such as unit profit. Because it can be applied to a wider spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes the execution of high-utility itemset mining into subtasks in order to leverage logical data parallelism, and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in the NUMA-aware manner. Our intensive and extensive experiments have confirmed that DPHIM performs up to 65.23 times faster than the fully-tuned serial execution, up to 23.54 times faster than static partitioning, and up to 2.51 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. As well, we have demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.07 to 2.43 times slower on persistent memory.