This paper describes COMPASS – a suite of irregular benchmarks comprising special patterns referred to as subscripted subscript patterns. When the values of an array appear at the subscript of another array in a for-loop, e.g. a[b[i]] with cross-iteration accesses to the host array (array a), such a pattern is referred to as a subscripted subscript pattern. These patterns represent an important class of dynamic, irregular memory access patterns observed in scientific applications and pose a challenge for optimizing compilers. The suite is a collection of subscripted subscript benchmarks from various application domains such as Machine Learning, Linear System Solvers, Adaptive Mesh Refinement, Sorting Algorithms and Sparse Matrix computations. The primary purpose of this suite is to promote the development of advanced compiler analysis and transformation techniques that enable parallelization of the subscripted subscript loops as well as techniques for improving locality and thread synchronization. We present the necessary and sufficient conditions for eventual parallelization of the subscripted subscript loops and discuss techniques described in the literature for determining said conditions. Experimental results show that subscripted subscript loops appear in key program sections and parallelizing them leads to a substantial improvement in the performance of the overall applications.