Hardware accelerators, such as GPUs, now provide a large part of the computational power used for scientific simulations. GPUs come with their own (limited) memory and are connected to the main memory of the machine via a bus with limited bandwidth. Scientific simulations often operate on very large data, to the point of not fitting in the limited GPU memory. In this case, one has to turn to out-of-core computing: data are kept in the CPU memory, and moved back and forth to the GPU memory when needed for the computation. This out-of-core situation also happens when processing on multicore CPUs with limited memory huge datasets stored on disk. In both cases, data movement quickly becomes a performance bottleneck. Task-based runtime schedulers have emerged as a convenient and efficient way to manage large applications on such heterogeneous platforms. They are in charge of choosing which tasks to assign on which processing unit and in which order they should be processed. In this work, we focus on this problem of scheduling for a taskbased runtime to improve data locality in an out-of-core setting, in order to reduce data movements. We design a data-aware strategy for both task scheduling and data eviction from limited memories. We compare this to existing scheduling techniques in runtime systems. Using the StarPU runtime, we show that our strategy achieves significantly better performance when scheduling tasks on multiple GPUs with limited memory, as well as on multiple CPU cores with limited main memory.