Meanwhile, HPC applications have evolved from numerical simulations to workloads that include Artificial Intelligence (AI) and analytics. For example, scientists at the Oak Ridge National Laboratory (ORNL) Health Data Sciences Institute are developing AI-based natural language processing tools to extract information from textual pathology reports using Summit, the USA’s most powerful supercomputer, due to the vast amounts of memory it provides to its compute cores. Similarly, the High Luminosity Large Hadron Collider (HL-LHC) will further extend the capabilities of the LHC, allowing further investigation of phenomena fundamental to the nature of the universe. To be installed in 2025, these enhancements will lead to annual data generation rates of tens of petabytes, with reduced datasets in the petabyte range being used for analysis. These applications are often read-intensive, and may rely on latency-sensitive transfers, each consisting of small amounts of data. This marks a dramatic shift in how HPC storage systems are used. While some emerging read-intensive workloads may be able to rely on structuring within the data to construct efficient data retrieval plans based on caching or prefetching techniques, AI workloads and many data analytics routines are inherently required to access the data without any predictable ordering. According to the Department of Energy’s 2020 AI for Science report \cite{Stevens_2020}:
    “AI training workloads, in contrast, must read large datasets (i.e., petabytes) repeatedly and     perhaps noncontiguously for training. AI models will need to be stored and dispatched to     inference engines, which may appear as small, frequent, random operations.”
Figure 1 shows examples of prevalent access patterns for analytics, which are characterized by this lack of ordering.
Two technology trends have emerged as crucial to data-driven scientific discovery. First, the high-speed networks used within scientific computing platforms provide extremely low-latency access to remote systems, including billions of message injections per second and direct access to remote system memory via remote direct memory access (RDMA) operations. Second, solid-state disks (SSDs) accessed through the Non-Volatile Memory Express (NVMe) interface provide more than 1,000 times the performance of traditional hard disk drives for the small random reads used within data-intensive workloads. Interestingly, while HPC storage systems broadly leverage both high-speed networks and SSDs, this adoption was not driven by the need to provide low-latency access to remote storage, but by simulation requirements for fast point-to-point communication between processes and high-throughput requirements for access to HPC storage systems. With the advent of new read-heavy analysis workloads, however, low-latency remote storage access is now also a key enabling technology for new data-driven approaches to computational science.