Non-uniform memory access (NUMA) is the mainstream memory access architecture for state-of-the-art multicore and multi-way processor platforms. Reducing the latency of cross-NUMA node accesses during queries is a key issue for modern in-memory database query optimization techniques. Due to the differences in NUMA architectures and NUMA latency across various processors, NUMA optimization techniques should be combined with hardware characteristics. This study focuses on the in-memory foreign key join algorithm, which has high cost and strong locality of data dependency in in-memory databases, and explores different NUMA optimization techniques, including NUMA-conscious and NUMA-oblivious implementations, on five platforms featuring ARM, Intel CLX/ICX, and AMD Zen2/Zen3 processors. The study also compares the performance of the algorithms across different processor platforms with strategies such as data storage, data partitioning, and join intermediate result caching. Experimental results show that the NUMA-conscious optimization strategy requires the integration of both software and hardware. Radix Join demonstrates neutral sensitivity to NUMA latency, with NUMA optimization gains constantly around 30%. The NPO algorithm shows higher sensitivity to NUMA latency, with NUMA optimization gains ranging from 38% to 57%. The Vector Join algorithm is sensitive to NUMA latency, but the impact is relatively minor, with NUMA optimization gains varying from 1% to 25%. For algorithm performance characteristics, cache efficiency influences the Vector Join performance more than NUMA latency. NUMA-conscious optimization techniques show significant differences on ARM platforms, while the differences are minimal on x86 platforms. The less complex NUMA-oblivious algorithms exhibit greater generality. Given hardware trends, reducing NUMA latency can effectively reduce performance gaps in NUMA-conscious optimization techniques, simplify join algorithm complexity, and improve join operation performance.