Related papers: The Los Alamos Computing Facility during the Manha…
The emergence of the microprocessor in the early 1970s allowed the design of computers that did not require the substantial economic resources of large computer companies of that era. Shortly after this event, a variety of computers based…
The Channel Archiver has been operational for more than two years at Los Alamos National Laboratory and other sites. This paper introduces the available components (data sampling engine, viewers, scripting interface, HTTP/CGI integration…
Quantum Computing (QC) has evolved from a few custom quantum computers, which were only accessible to their creators, to an array of commercial quantum computers that can be accessed on the cloud by anyone. Accessing these cloud quantum…
Processing in Memory (PIM) is a computing paradigm that promises enormous gain in processing speed by eradicating latencies in the typical von Neumann architecture. It has gained popularity owing to its throughput by embedding storage and…
This manuscript addresses selected aspects of computing for the reconstruction and simulation of particle interactions in subnuclear physics. Based on personal experience with experiments at DESY and at CERN, I cover the evolution of…
Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices provide a promising hardware substrate to build PIM…
The Daya Bay Reactor Neutrino Experiment started running on September 23, 2011. The offline computing environment, consisting of 11 servers at Daya Bay, was built to process onsite data. With current computing ability, onsite data…
The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size resource. In spite of the impressive scale of the existing…
Numerical QCD is often extremely resource demanding and it is not rare to run hundreds of simulations at the same time. Each of these can last for days or even months and it typically requires a job-script file as well as an input file with…
Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from…
Since the early 2000s there has existed the meme that "DOOM can run on anything". Whether it be an ATM or a calculator, someone at some point has recompiled DOOM to run on it. Now the quantum computer finally joins the list. More…
Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency…
Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the…
This essay gives a short, informal account of the development of digital logic from the Pleistocene to the Manhattan Project, the introduction of reversible circuits, and Richard Feynman's allied proposal for quantum computing. We argue…
Many modern and emerging applications must process increasingly large volumes of data. Unfortunately, prevalent computing paradigms are not designed to efficiently handle such large-scale data: the energy and performance costs to move this…
In-DRAM Processing-In-Memory (DRAM-PIM) has emerged as a promising approach to accelerate memory-intensive workloads by mitigating data transfer overhead between DRAM and the host processor. Bit-serial DRAM-PIM architectures, further…
The Latin American Giant Observatory (LAGO) project utilizes extensive High-Performance Computing (HPC) resources for complex astroparticle physics simulations, making resource efficiency critical for scientific productivity and…
High performance computing (HPC) user support teams are the first line of defense against large-scale problems, as they are often the first to learn of problems reported by users. Developing tools to better assist support teams in solving…
In Run 1 of the Large Hadron Collider, software and computing was a strategic strength of the Compact Muon Solenoid experiment. The timely processing of data and simulation samples and the excellent performance of the reconstruction…
This paper discusses recent research that aims to enable computation close to data, an approach we broadly call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside memory chips or…