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The Sparse Polyhedral Framework (SPF) provides vital support to scientific applications, but is limited in portability. SPF extends the Polyhedral Model to non-affine codes. Scientific applications need the optimizations SPF enables, but current SPF tools don’t support GPUs or other heterogeneous hardware targets. As clock speeds continue to stagnate, scientific applications need the performance enhancements enabled by both SPF and newer heterogeneous hardware. The MLIR (Multi-Level Intermediate Representation) ecosystem offers a large, extensible, and cooperating set of intermediate representations (called dialects). A typical compiler has one main intermediate representation, whereas an MLIR based compiler will have many. Because of this flexibility, the MLIR ecosystem has many dialects designed with heterogeneous hardware platforms in mind. This work creates an MLIR SPF dialect. The dialect enables SPF optimizations and is capable of generating GPU code as well as CPU code from SPF representations. Previous C based SPF front ends are not capable of generating GPU code. The SPF dialect representations of common sparse scientific kernels generate CPU code competitive with the existing C based front end, and GPU code competitive with standard benchmarks. 

Hydrologists must process many gigabytes of data for hydrologic simulations, which takes time and resources degrading performance. The performance issues are caused mainly by domain scientists’ preference for using Python, which trades performance for productivity. In my thesis, I demonstrate that using the static compilation technique to compile Python to generate C code along with several optimizations reduces time and resources for hydrologic data processing. I developed a Domain Specific Library (DSL) which is a subset of Python and compiles to Sparse Polyhedral Framework - Intermediate Representation (SPF-IR), which allows opportunities for optimizations like read reduction fusion which are not available in Python. We fused the file I/O to perform computation on small chunks of data (stream computation) in order to reduce the memory footprint. The C code we generated from SPF-IR shows an average speed-up of 2.58x over the existing hand-optimized implementations and can totally eliminate the tempo- rary storage required. DSL users can still enjoy the ease of use of Python but get performance better than the C code. 

Sparse computations are important in scientific computing. Many scientific applications compute on sparse data. Data is said to be sparse if it has a relatively small number of non-zeros. Sparse formats use auxiliary arrays to store non-zeros, as a result, the contents of auxiliary arrays are not known until run-time. The Inspector/Executor (I/E) paradigm uses run-time information for compiler optimizations. An inspector computes information at run-time to drive transformations. The executor—a compile-time transformation of the original code— uses information computed by the inspector. The sparse polyhedral framework (SPF) encompasses a series of tools to support I/E run-time transformations. This work introduces a unified framework that wraps SPF tools while providing a holistic view of computation as an intermediate representation (IR). This work also introduces a method to automatically synthesize inspectors to transform between sparse formats and improvements to SPF to explore the performance of irregular applications.