The challenge of student retention in introductory-level computing classes is a key contributor to the lack of diversity in computing fields. While teaching assistants (TAs) play a critical role in these classes, their role in supporting student retention is yet to be explored. We employed a special TA, a retention TA (RTA), who focuses exclusively on supporting students who are at risk of failing the course. RTAs target students who are retaking the class or underperforming in exams or assignments. This contrasts with regular TAs, who focus on grading and other standard duties. In Spring 2024, four RTAs were assigned to four different introductory-level computing CS1 and CS2 classes. At the end of the semester, we analyzed student attendance records and compared the performance of students who sought help from RTAs to the overall class performance. In two of the four classes, students who engaged with RTAs performed better than the class average. We share experiences from one of those classes, including specific RTA strategies that may have contributed to student success, such as personalized communication, reviewing course content, and helping students get started on assignments.

We develop from first principles an exact model of the behavior of loop nests executing in a memory hicrarchy, by using a nontraditional classification of misses that has the key property of composability. We use Presburger formulas to express various kinds of misses as well as the state of the cache at the end of the loop nest. We use existing tools to simplify these formulas and to count cache misses. The model is powerful enough to handle imperfect loop nests and various flavors of non-linear array layouts based on bit interleaving of array indices. We also indicate how to handle modest levels of associativity, and how to perform limited symbolic analysis of cache behavior. The complexity of the formulas relates to the static structure of the loop nest rather than to its dynamic trip count, allowing our model to gain efficiency in counting cache misses by exploiting repetitive patterns of cache behavior. Validation against cache simulation confirms the exactness of our formulation. Our method can serve as the basis for a static performance predictor to guide program and data transformations to improve performance.