Whilst FPGAs have been integrated in cloud ecosystems, strict constraints for mapping hardware to spatially diverse distribution of heterogeneous resources at run-time, makes their utilization for shared multi tasking challenging. This work aims at analyzing the efects of such constraints on the achievable compute density, i.e the efficiency in utilization of available compute resources. A hypothesis is proposed and uses static off-line partitioning and mapping of heterogeneous tasks to improve space sharing on FPGA. The hypothetical approach allows the FPGA resource to be treated as a service from higher level and supports multi-task processing, without the need for low level infrastructure support. To evaluate the effects of existing constraints on our hypothesis, we implement a relatively comprehensive suite of ten real high performance computing tasks and produce multiple bitstreams per task for fair evaluation of the various schemes. We then evaluate and compare our proposed partitioning scheme to previous work in terms of achieved system throughput. The simulated results for large queues of mixed intensity (compute and memory) tasks show that the proposed approach can provide higher than 3 x system speedup. The execution on the Nallatech 385 FPGA card for selected cases suggest that our approach can provide on average 2.9x and 2.3x higher system throughput for compute and mixed intensity tasks while 0.2x lower for memory intensive tasks.