In 2021, the Computer Science and Engineering department at UC Riverside added a second pathway to the major with a new CS1 and CS2 course sequence. Our goal was to address disparities in course outcomes between different populations, particularly between those with and without prior coding experience and majors versus non-majors. The hope was that students new to computing would have a viable path to discover whether they had interest and aptitude in computing without the added stress of being in a classroom with students who have prior coding experience. In this paper, we report the data analysis that led to our decision to add a second pathway, design choices, and challenges (particularly in university politics) in launching and adding a second pathway. We present the results over the last three years, which illustrate that the new series has leveled the playing field. Most importantly, the course outcome data and changes to computing major enrollment illustrate that we achieved our goal of attracting students new to computing and thus are broadening participation in computing.

Capstone or senior design courses are a key feature of most Computer Science programs as they provide students with authentic project experiences. Capstone courses usually focus on technical skill development, but we recognize that students will greatly benefit from developing communication skills. Within Computer Science, mastering such skills is essential for explaining and documenting system design, tackling development as a team, or leading large projects. Additionally, computer scientists need to be able to communicate their expertise to outside audiences such as funders, policymakers, customers, etc. However, studies have shown that students lack experience-based training to communicate functionally and practically. In this paper, we describe our experience with a targeted intervention within a capstone course in Computer Science. During the course, students complete a senior project while receiving training on team and project management, public speaking, and leadership. We evaluated the impact of the intervention on improving students' speaking skills and confidence by deploying surveys before and after the intervention. We observed a significant improvement in confidence and mastery of technical and non-technical oral communication skills. We also highlight how this training was particularly beneficial for underrepresented students within the discipline.

With the recent surge in interest in Computer Science, many challenges have emerged, ranging from faculty turnover to providing adequate resources and an inclusive environment for students throughout their academic journey. As part of our ongoing efforts, we introduce the concept of "Undergraduate Learning Assistants" (ULAs) - undergraduates carefully selected and trained by our department to serve as peer tutors for core Computer Science courses, such as CS1, CS2, and CS3. This program, driven not only by technical considerations but also by the goal of fostering a more inclusive and accessible learning community, seeks to enhance students' sense of belonging and encourage undergraduates to explore potential teaching roles. In this article, we share our experiences and present our assessments of this program.

Online learning is useful for expanding educational offerings. This is crucial for institutions with limited space and courses with high demand. Universities still need to be mindful about the quality of their online offerings, and ensure that it meets higher education standards. In this work we present a retrospective comparison between concurrent offerings (one online and one in classroom) of a discrete math course. The online course was carefully designed to be as challenging as the classroom one. We note the key takeaways from the experience.

Modern compiler optimization is a complex process that offers no guarantees to deliver the fastest, most efficient target code. For this reason, compilers struggle to produce a stable performance from versions of code that carry out the same computation and only differ in the order of operations. This instability makes compilers much less effective program optimization tools and often forces programmers to carry out a brute force search when tuning for performance. In this paper, we analyze the stability of the compilation process and the performance headroom of three widely used general purpose compilers: GCC, ICC, and Clang. For the study, we extracted over 1,000 for loop nests from well-known benchmarks, libraries, and real applications; then, we applied sequences of source-level loop transformations to these loop nests to create numerous semantically equivalent mutations ; finally, we analyzed the impact of transformations on code quality in terms of locality, dynamic instruction count, and vectorization. Our results show that, by applying source-to-source transformations and searching for the best vectorization setting, the percentage of loops sped up by at least 1.15x is 46.7% for GCC, 35.7% for ICC, and 46.5% for Clang, and on average the potential for performance improvement is estimated to be at least 23.7% for GCC, 18.1% for ICC, and 26.4% for Clang. Our stability analysis shows that, under our experimental setup, the average coefficient of variation of the execution time across all mutations is 18.2% for GCC, 19.5% for ICC, and 16.9% for Clang, and the highest coefficient of variation for a single loop nest reaches 118.9% for GCC, 124.3% for ICC, and 110.5% for Clang. We conclude that the evaluated compilers need further improvements to claim they have stable behavior.