FPGAs are becoming an attractive choice as a heterogeneous computing unit for scientific computing because FPGA vendors are adding floating-point-optimized architectures to their product lines. Additionally, high-level synthesis (HLS) tools such as Altera OpenCL SDK are emerging, which could potentially break the FPGA programming wall and provide a streamlined flow for domain experts in scientific computing. On the other hand, providing high performance in the presence of irregular memory access patterns to off-chip memory remains a challenge for the automated synthesis flows. In this paper, we study the performance/energy characteristics of OpenCL-generated FPGA designs on irregular memory access patterns, targeting XSBench, a memory-intensive Monte Carlo simulation code, as a case study. To complete our study, we implement XSBench in OpenCL and study optimization strategies for FPGAs. We observe that our OpenCL implantation of XSBench achieves 50 % higher energy efficiency on an Intel Arria10-based FPGA platform than that on an Intel Xeon 8-core CPU while trading off 35 % of performance.