Advanced Vector Extension (AVX) instructions operate on wide SIMD vectors. Due to the resulting high power consumption, recent Intel processors reduce their frequency when executing complex AVX2 and AVX-512 instructions. Following non-AVX code is slowed down by this frequency reduction in two situations: When it executes on the sibling hyperthread of the same core in parallel or - as restoring the non-AVX frequency is delayed - when it directly follows the AVX2/AVX-512 code. As a result, heterogeneous workloads consisting of AVX-512 and non-AVX code are frequently slowed down by 10% on average.

Over the last few years, Graphics Processing Units (GPUs) have become popular in computing, and have found their way into a number of cloud platforms. However, integrating a GPU into a cloud environment requires the cloud provider to efficiently virtualize the GPU. While several research projects have addressed this challenge in the past, few of these projects attempt to properly enable sharing of GPU memory between multiple clients: To date, GPUswap is the only project that enables sharing of GPU memory without inducing unnecessary application overhead, while maintaining both fairness and high utilization of GPU memory. However, GPUswap includes only a rudimentary swapping policy, and therefore induces a rather large application overhead.