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GPUs have become the primary choice of accelerators for high-end data centers and cloud servers, which can host thousands of disparate applications. With the growing demands for GPUs on clusters, there arises a need for efficient co-execution of applications on the same accelerator device. However, the resource contention among co-executing applications causes interference which leads to degradation in execution performance, impacts QoS requirements of applications and lowers overall system throughput. While previous work has proposed techniques for detecting interference, the existing solutions are either developed for CPU clusters, or use static profiling approaches which can be computationally intensive and do not scale well. We present Mystic, an interference-aware scheduler for efficient co-execution of applications on GPU-based clusters and cloud servers. The most important feature of Mystic is …