Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions more easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many possible settings. My research addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. In this talk, I will present tools for deterministic parallel programming, large-scale shared-memory algorithms that are efficient both in theory and in practice, and Ligra, a framework for simplifying the programming of shared-memory graph algorithms.