The field of computer architecture is witnessing significant advancements in cache analysis and GPU architecture. Researchers are focusing on developing innovative methods for quantitative cache analysis, which can be applied to various cache policies and microarchitectures. This has led to a better understanding of cache behavior and improved real-time guarantees. Additionally, there is a growing interest in optimizing GPU memory hierarchies to increase memory bandwidth and reduce bottlenecks. Novel arbitration strategies and multiport memory hierarchies are being proposed to maximize memory bandwidth and improve performance. Furthermore, new analytical models are being developed to identify shared-memory atomic bottlenecks and improve the performance of GPU programs. These advances are expected to have a significant impact on the development of high-performance computing systems. Noteworthy papers include:

• A Unified Framework for Quantitative Cache Analysis, which presents a unified framework for quantitative cache analysis that can be applied to non-LRU policies.
• Multiport Support for Vortex OpenGPU Memory Hierarchy, which proposes a multiport memory hierarchy for GPU architectures to increase memory bandwidth.
• Analyzing Modern NVIDIA GPU cores, which reverse engineers modern NVIDIA GPU cores and reveals key aspects of its design, leading to improved simulation accuracy and performance.