Large graphs are widely used in real world graph analytics. Memory available in a single machine is usually inadequate to process these graphs. A good solution is to use a distributed environment. Typical programming styles used in existing distributed environment frameworks are different from imperative programming and difficult for programmers to adapt. Moreover, some graph algorithms having a high degree of parallelism ideally run on an accelerator cluster. Error prone and lower level programming methods (memory and thread management) available for such systems repel programmers from using such architectures. Existing frameworks do not deal with the accelerator clusters. We propose a framework which addresses the previously stated deficiencies. Our framework automatically generates implementations of graph algorithms for distributed environments from the intuitive shared memory based code written in a high-level Domain Specific Language (DSL), Falcon. The framework analyses the intermediate representation, applies a set of optimizations and then generates Giraph code for a CPU cluster and MPI+OpenCL code for a GPU cluster. Experimental evaluations show efficiency and scalability of our framework. © Springer International Publishing AG, part of Springer Nature 2018.