Computation offloading is a programming model in which program fragments (e.g. hot loops) are annotated so that their execution is performed in dedicated hardware or accelerator devices. Although offloading has been extensively used to move computation to GPUs, through directive-based annotation standards like OpenMP, offloading computation to very large computer clusters can become a complex and cumbersome task. It typically requires mixing programming models (e.g. OpenMP and MPI) and languages (e.g. C/C++ and Scala), dealing with various access control mechanisms from different clouds (e.g. AWS and Azure), and integrating all this into a single application. This paper introduces the cloud as a computation offloading device. It integrates OpenMP directives, cloud based map-reduce Spark nodes and remote communication management such that the cloud appears to the programmer as yet another device available in its local computer. Experiments using LLVM, OpenMP 4.5 and Amazon EC2 show the viability of the proposed approach and enable a thorough analysis of the performance and costs involved in cloud offloading. The results show that although data transfers can impose overheads, cloud offloading can still achieve promising speedups of up to 86x in 256 cores for the 2MM benchmark using 1GB matrices.