OpenCL is undoubtedly becoming one of the most popular parallel programming languages as it provides a standardized and portable programming model. However, adopting OpenCL for Coarse-Grained Reconfigurable Arrays (CGRA) is challenging due to divergent architecture capability compared to GPUs. In particular, CGRAs are designed to accelerate loop execution by software pipelining on a grid of functional units exploiting instruction-level parallelism. This is vastly different from a GPU in that it executes data parallel kernels using a large number of parallel threads. Therefore, an OpenCL compiler and runtime for CGRAs must map the threaded parallel programming model to a loop-parallel execution model so that the architecture can best utilize its resources. In this paper, we propose and evaluate a design for an OpenCL compiler framework for CGRAs. The proposed design is composed of a serializer and post optimizer. The serializer transforms parallel execution of work-items to an equivalent loop-based iterative execution in order to avoid expensive multithreading on CGRAs. The resulting code is further optimized by the post optimizer to maximize the coverage of software-pipelinable innermost loops. In order to achieve the goal, various loop-level optimizations can take place in the post optimizer using the loops introduced by the serializer for iterative execution of OpenCL kernels. We provide an analysis of the propose framework from a set of well-studied standard OpenCL kernels by comparing performance of various implementations of benchmarks.