# hpc Published articles for hpc. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## DoE seeking fault-tolerant quantum computer ... by 2028 DevFeed: [DoE seeking fault-tolerant quantum computer ... by 2028]() Original publisher: [Read original article]() Author: Brandon Vigliarolo Published: 2026-09-17T20:09:45Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Quantum Computing](), [Computing]() Tags: [department-of-energy](), [hpc](), [quantum](), [quantum-computing](), [qubit]() ### AI overview The document describes a quantum-computing effort in which entrants receive $250,000 upfront to deliver a demonstration within a year. ### Source excerpt Entrants get $250K upfront to deliver a demo within a year, which sounds totally feasible given quantum computing's track record ## Fujitsu ready to sell its custom 'Monaka' Arm chip, maybe to rival server-makers DevFeed: [Fujitsu ready to sell its custom 'Monaka' Arm chip, maybe to rival server-makers]() Original publisher: [Read original article]() Author: Simon Sharwood Published: 2026-09-17T06:20:33Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Arm](), [Server]() Tags: [arm](), [fujitsu](), [hpc](), [inference](), [server](), [sovereign-cloud](), [supercomputer](), [systems]() ### AI overview The article reports that Fujitsu is preparing to sell its custom Monaka Arm chip to other server makers, with potential interest from cloud and inference-focused customers. ### Source excerpt Clouds, the sovereign-sensitive, and the inferencing-interested are also about to get sales calls ## Cleveland Clinic, RIKEN, IBM named Gordon Bell finalists DevFeed: [Cleveland Clinic, RIKEN, IBM named Gordon Bell finalists]() Original publisher: [Read original article]() Published: 2026-09-09T04:00:00Z Content type: news Language: en Sources: [IBM Research]() Topics: [High-Performance Computing](), [ibm](), [Supercomputing](), [Benchmark](), [data]() Tags: [automated](), [chemistry](), [gpu](), [hpc](), [ibm](), [news](), [quantum](), [quantum-community](), [quantum-network](), [quantum-research](), [recognition](), [research](), [supercomputing](), [workflows]() ### AI overview Cleveland Clinic, RIKEN, and IBM were named finalists for the 2026 ACM Gordon Bell Prize for quantum-HPC chemistry research. The collaboration simulated a 12,635-atom protein system and reported an automated workflow that reduces coordination and data movement across quantum and classical computing resources. ### Source excerpt Finalist recognition for one of supercomputing's top prizes arrives as researchers report new progress in automated quantum-HPC chemistry workflows. ## Hot Chips 2026: Fujitsu's Monaka CPU DevFeed: [Hot Chips 2026: Fujitsu's Monaka CPU]() Original publisher: [Read original article]() Author: Chester Lam Published: 2026-08-26T01:20:30Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [cpu](), [Arm](), [Algorithm](), [intel]() Tags: [2026](), [algorithm](), [amd](), [arm](), [cpu](), [fujitsu](), [hpc](), [intel](), [processor]() ### AI overview The article examines Fujitsu's Monaka CPU, which is intended to improve on A64FX's limitations for general-purpose workloads while retaining strong HPC vector throughput. It discusses Monaka's three-level TAGE branch predictor and contrasts it with A64FX's perceptron-like predictor. ### Source excerpt Building on a long history of HPC-focused cores, and looking beyond HPC ## Scaling ML Training on Kubernetes with JobSet DevFeed: [Scaling ML Training on Kubernetes with JobSet]() Original publisher: [Read original article]() Author: Abhimanyu Saharan Published: 2025-05-05T00:00:00Z Content type: article Language: en Sources: [Abhimanyu Saharan]() Topics: [Kubernetes](), [Machine learning](), [API](), [jobs]() Tags: [api](), [distributed](), [hpc](), [jobs](), [kubernetes](), [ml](), [training]() ### AI overview The article introduces JobSet, a Kubernetes-native API for managing distributed machine learning and high-performance computing jobs. It highlights multi-role pods, topology-aware placement, and scaling capabilities. ### Source excerpt JobSet is a Kubernetes-native API for managing distributed ML and HPC jobs with support for multi-role pods, topology-aware placement, and scaling. ## Thread Count Scaling Part 4. CloverLeaf and CPython DevFeed: [Thread Count Scaling Part 4. CloverLeaf and CPython]() Original publisher: [Read original article]() Author: Denis Bakhvalov Published: 2024-05-10T04:00:00Z Content type: article Language: en Sources: [Denis Bakhvalov]() Topics: [benchmarking](), [Concurrency]() Tags: [book-chapters](), [case-study](), [dram](), [hpc](), [memory](), [metric](), [performance](), [performance-analysis](), [scale](), [speed](), [thread]() ### AI overview The article examines CloverLeaf and CPython thread-count scaling. It reports that CloverLeaf performance stops increasing after three threads because memory bandwidth becomes the limiting factor. Replacing two memory modules with faster DDR4 modules improves performance by 10% to 33% as thread count increases. ### Source excerpt Subscribe to my newsletter, support me on Patreon, Github, or by PayPal donation. This blog is an excerpt from the book. More details in the introduction. CloverLeaf is a hydrodynamics workload. We will not dig deep into the details of the underlying algorithm as it is not relevant to this case study. CloverLeaf uses OpenMP to parallelize the workload. Similar to other HPC workloads, we should expect CloverLeaf to scale well. ## Data Science with Nix: Parameter Sweeps DevFeed: [Data Science with Nix: Parameter Sweeps]() Original publisher: [Read original article]() Author: support@nixbuild.net Published: 2021-04-26T00:00:00Z Content type: tutorial Language: en Sources: [nixbuild.net blog]() Topics: [Nix](), [Data Science](), [Simulation](), [builds]() Tags: [benchmarking](), [data-science](), [hpc](), [parameter](), [simulation](), [software]() ### AI overview This practical article explains parameter sweeps in scientific computing and their equivalent in software build matrices. It demonstrates how Nix and nixbuild.net can manage tasks involving many input-parameter combinations, including simulations, builds, and benchmarks. ### Source excerpt Parameter sweeping is a technique often utilized in scientific computing and HPC settings. In the mainstream software industry the concept is called a build matrix. The idea is that you have a task you want to perform with varying input parameters. If the task takes multiple parameters, and you'd like to try it out with multiple values for each parameter, it is easy to end up with a combinatorial explosion. This blog post gives a practical demonstration showing how Nix is a perfect companion for managing parameter sweeps and build matrices, and how nixbuild.net can be used to supercharge your workflow. My hope is that this text can interest readers that don't know anything about Nix as well as experienced Nix users. Use Cases In scientific computing, it is common to run simulations of physical processes. The list of things simulated is endless: weather forecasting, molecular dynamics, celestial movements, FEM analysis, particle physics etc. A simulation is usually implemented directly as a computer program or as a description for a higher level simulation framework. A simulation generally has a set of input parameters that can be defined. These parameters can describe initial states, environmental aspects or tweak the behavior of the simulation algorithm itself. Scientists are interested in comparing simulation results for a range of different parameter values, and the process of doing so is referred to as a parameter sweep. Parameter sweeping is often built into simulation frameworks. For simulations implemented directly as specialized programs, scientists will simply run the program over and over again with different parameters, collecting and comparing the results. When supercomputers are used for running the simulations, the job scheduler usually has some support for launching multiple simulation instances with varying parameters. In the software industry, the term build matrix is used to mean basically the same thing as parameter sweeping. Regularly, build matr ## cPouta Flavour Pikkujoulu 2018 Communiqué DevFeed: [cPouta Flavour Pikkujoulu 2018 Communiqué]() Original publisher: [Read original article]() Author: Unknown (noreply@blogger.com) Published: 2018-11-26T14:44:00Z Content type: release Language: en Sources: [CSC - IT Center For Science - Cloud Team]() Topics: [cloud-infrastructure](), [ceph](), [Hardware]() Tags: [capacity](), [ceph](), [cpouta](), [energy](), [hardware](), [hpc](), [standard](), [storage]() ### AI overview The communiqué announces changes to cPouta VM flavours: several older HPC flavours will no longer be available for new launches, while new hpc.4 and standard.xxlarge flavours will be added. The hpc.4 flavours use Ceph for VM root-disk backing storage, replacing older hardware without adding cPouta capacity. ### Source excerpt We are making some changes to the cPouta flavours offering tl;dr You won't be able to launch _new_ hpc-gen1.*core, hpc-gen2.2core, hpc-gen2.8core, hpc-gen2.16core flavours. We are adding hpc.4 flavours. We are adding a standard.xxlarge flavour. The new hpc.4 flavours The hpc.4 flavours are similar to the similarly named flavour in ePouta. The big difference between the hpc-genX is that hpc.4 will be using Ceph as default backing storage for VM root disks. We have wholesome experiences from Ceph in ePouta where all hpc-flavours are using Ceph as backing storage. One of the benefit is that if a hypervisor breaks, we can either migrate the customer instances away without customer noticing anything, or if the hypervisor has catastrophic failure the instance can be booted up on a new hypervisor without loss of data. Furthermore the sequential reads are much faster than on local spinning disks. This change will not bring additional capacity in cPouta, but replace old energy inefficient hardware in-kind with newer hardware. The new bigger standard flavour standard.xxlarge The standard flavours are the most popular flavours by a huge margin. An interesting thing that we have noticed is that if you combine the amount of hpc-gen2.8core and hpc-gen1.8core flavours that would be the second most popular flavour. We belivie that many will prefer to use standard.xxlarge instead of hpc flavours with similar amount of RAM. The new flavours hpc.4.5core - 5 cores 22GB RAM - replacement for hpc-gen1.1core, hpc-gen1.4core, alternative to hpc-gen2.2core, hpc.4.10core - 10 cores 44GB RAM - replacement for hpc-gen1.8core, alternative to hpc-gen2.8core hpc.4.20core - 20 cores 88GB RAM - replacement for hpc-gen1.16core, alternative to hpc-gen2.16core and hpc-gen2.24core standard.xxlarge - 8 oversubscribed cores, 32GB RAM - alternative to hpc-gen2.8core, hpc-gen1.8core, hpc4.5core and hpc.4.10core Why are the hpc-gen1 flavours disappearing? Existing instances of these flavour will continue to ## ePouta Flavour Halloween 2018 Communiqué DevFeed: [ePouta Flavour Halloween 2018 Communiqué]() Original publisher: [Read original article]() Author: Unknown (noreply@blogger.com) Published: 2018-10-19T10:51:00Z Content type: release Language: en Sources: [CSC - IT Center For Science - Cloud Team]() Topics: [GPU](), [cpu](), [Nvidia]() Tags: [applications](), [cpu](), [deprecated](), [epouta](), [espoo](), [flavors](), [gpu](), [hpc](), [newton](), [numa](), [nvidia](), [openstack](), [performance](), [pike](), [pouta](), [standard](), [westmere]() ### AI overview The communiqué announces changes to ePouta flavors, including public availability of the gpu.2.1gpu flavor with an NVIDIA Tesla V100 and NUMA-aware CPU and memory behavior. It also announces the deprecation of hpc.*.westmere flavors and recommends replacement flavors for new instances. ### Source excerpt We are making some changes to our flavor offering . Here's a summary of the changes: We now have even more GPUs in ePouta. The gpu.2.1gpu flavor is now publicly available and each has one NVIDIA Tesla V100 via PCI pass through. This flavor was already added in July but could only be accessed after requesting it from servicedesk. Now we are making them publicly available. The gpu.2.1gpu flavor is our first NUMA aware flavor. This should speed up CPU & memory heavy applications. Our current version (Newton) of OpenStack is not able to guarantee PCI devices from the same NUMA node too, this is possible to come when we get to Pike or later. Deprecation of hpc.*.westmere flavors This does not affect running instances but in the future you might see changes in the performance and therefor we suggest that you resize your flavor to a newer flavor type. You won't be able to launch new instances with the deprecated flavors. Flavors that will be deprecated and suggested replacement: hpc.medium.westmere - standard.xlarge hpc.large.westmere - hpc.4.10core hpc.xlarge.westmere - hpc.4.10core or hpc.4.20core hpc.largemem.westmere - hpc.4.20core or hpc.3.28core More information about the different flavors can be found here: https://research.csc.fi/pouta-flavours No known Copyright Please acknowledge 'Sir George Grey Special Collections, Auckland Libraries, 1-W682' when re-using this image. ## ePouta adds new VM flavors and server hardware DevFeed: [ePouta adds new VM flavors and server hardware]() Original publisher: [Read original article]() Author: Unknown (noreply@blogger.com) Published: 2018-07-03T09:23:00Z Content type: release Language: en Sources: [CSC - IT Center For Science - Cloud Team]() Topics: [cloud-infrastructure](), [virtual machines](), [Hardware](), [cpu](), [Nvidia]() Tags: [availability](), [cpu](), [epouta](), [espoo](), [flavors](), [gpu](), [hardware](), [hpc](), [nvidia](), [openstack](), [pouta](), [virtual-machines]() ### AI overview CSC announces new VM flavors and server hardware for ePouta, including standard flavors, Skylake-based HPC flavors, a 735 GB big-memory flavor, and NVIDIA Tesla V100 GPU instances available by request. ### Source excerpt We are pleased to announce the immediate availability of new lovely VM flavors in ePouta backed by new server hardware! A summary of the changes: introduced standard-flavors to ePouta; these are the most popular flavors in cPouta. introduced a new generation of hpc-flavors which have Skylake CPUs. a new generation of big memory flavor with 700GB of RAM. GPUs in ePouta. The flavors have 1 NVIDIA Tesla V100. These are only available per request to CSC Service Desk. deprecating hpc.mini, hpc.small and all Haswell hpc-flavors except hpc.fullnode.haswell. This does not affect the io.haswell-flavors. As always the current description of flavors can be found on https://research.csc.fi/pouta-flavours New Flavors The new standard flavors are of the same configuration that already exists in cPouta. This flavor is really cheap but not suitable for heavy workloads since the CPU is oversubscribed. This means that many virtual machines have to share the same CPUs. These instances are very good for running IT-services, web-servers, and doing development with. There will be standard.*-flavors (for example standard.small) with between 1GB and 16GB of RAM. These are the cheapest flavors we have in ePouta. These will most-likely be your go-to flavor for non-heavy workloads. We are introducing hpc4 flavors, the next generation of HPC flavors. They are named hpc.4.80cores, hpc.4.40cores, hpc.4.20cores and hpc.4.10cores. These are very similar to the hpc.3.* flavors, with additional sizes and the newer Skylake CPU generation. The biggest instance has 50% more cores and memory (45, 90, 180 and 360 GB) than the previous generation. These flavors will probably be your go-to flavor for computational workloads. The new big memory flavor is called tb.4.735RAM and has 80 hyper-threaded Skylake cores, 735GB of memory and a whopping 2500GB of local SSD disk space. The SSD disks are configured in a RAID0 configuration which means that they are really fast but if the disk fails your data will be lo