Simultaneous multithreading (SMT) processors improve system throughput by sharing core resources among the threads running on the same core. However, intra-core interference can cause co-running applications to degrade each other’s performance significantly. To address this issue, some approaches have focused on balancing contention at the core shared resources (e.g. the shared L1 data cache). A key advantage of these approaches is that they are workload-agnostic. Other approaches improve the previous ones by modeling the inter-application interference across the intra-core shared resources. Unfortunately, these approaches require off-line model training for specific workloads. This paper presents WAPA, a CPI-based thread-to-core allocation approach that incorporates the best of both worlds. WAPA is a workload-agnostic policy that implicitly accounts for inter-thread interference across all the shared resources by leveraging the CPI. The proposed approach relies on the optimal transport (OT) theory, a mathematical theory to dynamically select symbiotic pairs of applications. Experimental results in an Intel Xeon show that WAPA outperforms the default Linux scheduler on average by 8.4% in IPC in workloads dominated by cache and main memory latencies, while performance gains of existing approaches are below 3.2%.