Computing centres, including those used to process High-Energy Physics data and simulations, are increasingly providing significant fractions of their computing resources through hardware architectures other than x86 CPUs, with GPUs being a common alternative. GPUs can provide excellent computational performance at a good price point for tasks that can be suitably parallelized. Charged particle (track) reconstruction is a computationally expensive component of HEP data reconstruction, and thus needs to use available resources in an efficient way. In this paper, an implementation of Kalman filter-based track fitting using CUDA and running on GPUs is presented. This utilizes the ACTS (A Common Tracking Software) toolkit; an open source and experiment-independent toolkit for track reconstruction. The implementation details and parallelization approach are described, along with the specific challenges for such an implementation. Detailed performance benchmarking results are discussed, which show encouraging performance gains over a CPU-based implementation for representative configurations. Finally, a perspective on the challenges and future directions for these studies is outlined. These include more complex and realistic scenarios which can be studied, and anticipated developments to software frameworks and standards which may open up possibilities for greater flexibility and improved performance.

SFI's scope covers innovative HPC technologies, prospective architectures, scalable & highly parallel algorithms, languages, data analytics, computational codesign, supercomputing education, massively parallel computing applications in science & industry.

The dynamic evolution of non-volatile memory (NVM) technologies from Read-Only Memory (ROM) to flash memory, and recent innovations in Magnetoresistive RAM (MRAM), Phase Change Memory (PCM), and Resistive RAM (ReRAM) signify a pivotal shift in data storage capabilities and applications. This progression, marked by enhancements in performance and density, fills the gap between Dynamic RAM (DRAM) and flash storage, meeting the demands of data-rich domains such as high-performance computing and databases. The integration of NVM like the formerly commercially available Optane Persistent Memory (PMem) has introduced a paradigm shift in storage system design, addressing challenges related to latency, data proximity, memory constraints, and energy efficiency which are critical in computing. Despite facing hurdles, such as performance asymmetry in Optane PMem, the potential of NVM in data storage systems has been shown with research focusing on speeding up indexing, small data accesses, and cache-less I/O paths. We review existing and emerging NVM technologies based on their physical properties and application in data storage. We underscore the significance of NVM in offering unique access characteristics that address the limitations of traditional storage devices, thereby extending the data storage landscape. We discuss the programming challenges and hardware-software strategies crucial for NVM’s seamless integration and widespread industry adoption. Additionally, we discuss the discontinuation of Optane, the ongoing challenges and the need for storage system designs to adapt to non-block based storage to leverage NVM’s performance benefits. We conclude that while advancements in NVM promise improved performance and endurance, practical availability depends on addressing challenges such as density, heat management, and reliability. Support in the existing software ecosystem is also crucial for its widespread adoption. Furthermore, Compute Express Link (CXL) is highlighted as a significant development that can streamline the memory hierarchy and support the adoption of NVM, enabling more flexible designs and effective usage of storage media in next-generation data storage systems. Overall, CXL offers a promising method to include NVM in future systems. However, in the meantime, data storage systems will rely on NVMe SSDs due to their performance and availability. But, we encourage further research into integrating NVM devices due to data management constraints with conventional block-based data management and advocate for the availability of NVM-ready software to users.