Concurrent B+trees have been widely used in many systems from file systems to databases. With the requests expanding exponentially, the systems are facing tremendous performance pressure. GPUs have shown their potential to accelerate the concurrent B+trees operations with their high volume of parallel computing resources and large memory bandwidth. In concurrent B+tree, conflict detection and handling complicates the request processing logic, increases the number of memory accesses and leads to execution path divergence. That leads to performance degradation and increased response time variance. In this paper, we present a GPU concurrent B+tree. To reduce the performance impact introduced by conflict detection, an optimization is proposed to detect and resolve key conflicts before concurrent requests are issued to the execution phase. Then another optimization is used to reduce structure conflicts and concurrent control overheads. Finally, an optimization is used to reduce structure conflicts and improve memory performance by exploiting the locality among requests. Evaluations on a TITAN V GPU show that Eirene is efficient with a throughput of 1.23 billion per second. Compared to a state-of-the-art GPU B+tree system, it can achieve 5.1X speedup and reduce the response time variance from 38% to 4%.