Virtual reality (VR) technology has emerged as a promising tool in education, offering new possibilities for learning in immersive environments. This paper explores the application of VR in biology education, specifically focusing on teaching the functionality of the cell membrane. The traditional methods of textbook reading and microscopic observation lack interactivity and engagement, motivating the development of a VR-based learning approach designed as a game-like experience. The study compares the knowledge gain of participants who played the VR game with a control group who watched instructional videos. Surprisingly, the control group exhibited a higher knowledge gain compared to the VR group. Analysis suggests that the VR participants were more distracted by the immersive nature of the VR environment and focused less on the instructional content. Future research involving larger and more motivated participant groups, such as freshmen or high school students, may yield different results. Despite the observed limitations, this study highlights the importance of balancing immersive experiences with focused instructional content for better learning outcomes.

Hardware prefetching is one of the common off-chip DRAM latency hiding techniques. Though hardware prefetchers are ubiquitous in the commercial machines and prefetching techniques are well studied in the computer architecture community, the “memory wall” problem still exists after decades of microarchitecture research and is considered to be an essential problem to solve. In this paper, we make a case for breaking the memory wall through data prefetching at the L1 cache.We propose a bouquet of hardware prefetchers that can handle a variety of access patterns driven by the control flow of an application. We name our proposal Instruction Pointer Classifier based spatial Prefetching (IPCP). We propose IPCP in two flavors: (i) an L1 spatial data prefetcher that classifies instruction pointers at the L1 cache level, and issues prefetch requests based on the classification, and (ii) a multi-level IPCP where the IPCP at the L1 communicates the classification information to the L2 IPCP so that it can kick-start prefetching based on this classification done at the L1. Overall, IPCP is a simple, lightweight, and modular framework for L1 and multi-level spatial prefetching. IPCP at the L1 and L2 incurs a storage overhead of 740 bytes and 155 bytes, respectively.Our empirical results show that, for memory-intensive single-threaded SPEC CPU 2017 benchmarks, compared to a baseline system with no prefetching, IPCP provides an average performance improvement of 45.1%. For the entire SPEC CPU 2017 suite, it provides an improvement of 22%. In the case of multicore systems, IPCP provides an improvement of 23.4% (evaluated over more than 1000 mixes). IPCP outperforms the already high-performing state-of-the-art prefetchers like SPP with PPF and Bingo by demanding 30X to 50X less storage.