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Modern scientific repositories are growing rapidly in size. Scientists are increasingly interested in viewing the latest data as part of query results. Current scientific middleware cache systems, however, assume repositories are static.…
The growing need for continuous processing capabilities has led to the development of multicore systems with a complex cache hierarchy. Such multicore systems are generally designed for improving the performance in average case, while hard…
Hash tables are an essential data-structure for numerous networking applications (e.g., connection tracking, firewalls, network address translators). Among these, cuckoo hash tables provide excellent performance by allowing lookups to be…
Application-level caching is a form of caching that has been increasingly adopted to satisfy performance and throughput requirements. The key idea is to store the results of a computation, to improve performance by reusing instead of…
Edge inference is a technology that enables real-time data processing and analysis on clients near the data source. To ensure compliance with the Service-Level Objectives (SLOs), such as a 30% latency reduction target, caching is usually…
Query processing over big data is ubiquitous in modern clouds, where the system takes care of picking both the physical query execution plans and the resources needed to run those plans, using a cost-based query optimizer. A good cost…
With the ever-increasing dataset sizes, several file formats like Parquet, ORC, and Avro have been developed to store data efficiently and to save network and interconnect bandwidth at the price of additional CPU utilization. However, with…
The in-memory cache system is an important component in a cloud for the data access performance. As the tenants may have different performance goals for data access depending on the nature of their tasks, effectively managing the memory…
Many HPC applications suffer from a bottleneck in the shared caches, instruction execution units, I/O or memory bandwidth, even though the remaining resources may be underutilized. It is hard for developers and runtime systems to ensure…
Large Language Models (LLMs) with expanding context windows face significant performance hurdles. While caching key-value (KV) states is critical for avoiding redundant computation, the storage footprint of long-context caches quickly…
Recent studies have demonstrated that near-data processing (NDP) is an effective technique for improving performance and energy efficiency of data-intensive workloads. However, leveraging NDP in realistic systems with multiple memory…
In parallel with big data processing and analysis dominating the usage of distributed and cloud infrastructures, the demand for distributed metadata access and transfer has increased. In many application domains, the volume of data…
The sheer number of nodes continues to increase in todays supercomputers, the first half of Trinity alone contains more than 9400 compute nodes. Since the speed of todays clusters are limited by the slowest nodes, it more important than…
Major chip manufacturers have all introduced multicore microprocessors. Multi-socket systems built from these processors are used for running various server applications. Depending on the application, remote cache-to-cache transfers can…
We study the problem of constructing concurrent objects in a setting where $P$ processes run in parallel and interact through a shared memory that is subject to write contention. Our goal is to transform hardware primitives that are subject…
Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-value (KV) caches in the prefill stage presents a major…
Stencil computation is one of the most used kernels in a wide variety of scientific applications, ranging from large-scale weather prediction to solving partial differential equations. Stencil computations are characterized by three unique…
Reducing the average memory access time is crucial for improving the performance of applications running on multi-core architectures. With workload consolidation this becomes increasingly challenging due to shared resource contention.…
Increasing amounts of data from varied sources, particularly in the fields of machine learning and graph analytics, are causing storage requirements to grow rapidly. A variety of technologies exist for storing and sharing these data,…
In recent years, SSDs have gained tremendous attention in computing and storage systems due to significant performance improvement over HDDs. The cost per capacity of SSDs, however, prevents them from entirely replacing HDDs in such…