Prolonged screen time while working on programming assignments can often lead to student fatigue, boredom, and stress. In response to these issues and to enhance the overall programming environment for students, we introduce ThemeRec, a Visual Studio Code extension. ThemeRec provides students with personalized recommendations for code themes, with a particular focus on semantic coloring combinations. It boasts an extensive collection of over 150 code themes, each offering unique combinations of background colors, font styles, and semantic color arrangements. Using an integrated collaborative filtering recommendation system, ThemeRec automatically suggests and installs various themes based on the preferences of similar users whenever they want to change a theme. To assess the effectiveness of ThemeRec, we conducted an evaluation within an introductory-level programming course. The feedback from students has been positive and encouraging. This underscores the potential value that ThemeRec brings to programming courses.

We present the design and the implementation of a kernel principal component regression software that handles training datasets with a million or more observations. Kernel regressions are nonlinear and interpretable models that have wide downstream applications, and are shown to have a close connection to deep learning. Nevertheless, the exact regression of large-scale kernel models using currently available software has been notoriously difficult because it is both compute and memory intensive and it requires extensive tuning of hyperparameters.

Python has become a popular programming language because of its excellent programmability. Many modern software packages utilize Python for high-level algorithm design and depend on native libraries written in C/C++/Fortran for efficient computation kernels. Interaction between Python code and native libraries introduces performance losses because of the abstraction lying on the boundary of Python and native libraries. On the one side, Python code, typically run with interpretation, is disjoint from its execution behavior. On the other side, native libraries do not include program semantics to understand algorithm defects.

Active development in new memory devices, such as non-volatile memories and high-bandwidth memories, brings heterogeneous memory systems (HMS) as a promising solution for implementing large-scale memory systems with cost, area, and power limitations. Typical HMS consists of a small-capacity high-performance memory and a large-capacity low-performance memory. Data placement on such systems plays a critical role in performance optimization. Existing efforts have explored coarse-grained data placement in applications with dense data structures; however, a thorough study of applications that are based on graph data structures is still missing.

Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkpoint constructions (called LC-Checkpoint). LC-Checkpoint simultaneously maximizes the compression rate and optimizes the recovery speed, under the assumption that SGD is used to train the model. LC-Checkpoint uses quantization and priority promotion to store the most crucial information for SGD to recover, and then uses a Huffman coding to leverage the non-uniform distribution of the gradient scales. Our extensive experiments show that LC-Checkpoint achieves a compression rate up to 28{\texttimes} and recovery speedup up to 5.77{\texttimes} over a state-of-the-art algorithm (SCAR).