122: GreyBeards talk big data archive with Floyd Christofferson, CEO StrongBox Data Solutions

The GreyBeards had a great discussion with Floyd Christofferson, CEO, StrongBox Data Solutions on their big data/HPC file and archive solution. Floyd’s is very knowledgeable on problems of extremely large data repositories and has been around the HPC and other data intensive industries for decades.

StrongBox’s StrongLink solution offers a global namespace file system that virtualizes NFS, SMB, S3 and Posix file environments and maps this to a software-only, multi-tier, multi-site data repository that can span onsite flash, disk, S3 compatible or Azure object and LTFS tape iibrary storage as well as offsite versions of all the above tiers.

Typical StrongLink customers range in the 10s to 100s of PB, and ingesting or processing PBs a day. 200TB is a minimum StrongLink configuration, but Floyd said any shop with over 500TB has problems with data silos and other issues, but may not understand it yet. StrongLink manages data placement and movement, throughout this hierarchy to better support data access and economical storage. In the process StrongLink eliminates any data silos due to limitations of NAS systems while providing the most economic placement of data to meet user performance requirements.


Floyd said that StrongLink first installs in customer environment and then operates in the background to discover and ingest metadata from the primary customers file storage environment. Some point later the customer reconfigures their end-users share and mount points to StrongLink servers and it’s up and starts running.

The minimal StrongLink, HA environment consists of 3 nodes. They use a NoSQL metadata database which is replicated and sharded across the nodes. It’s shared for performance load balancing and fully replicated (2-way or 3-way) across all the StrongLink server nodes for HA.

The StrongLink nodes create a cluster, called a star in StrongBox vernacular. Multiple clusters onsite can be grouped together to form a StrongLink constellation. And multiple data center sites, can be grouped together to form a StrongLink galaxy. Presumably if you have a constellation or a galaxy, the same metadata is available to all the star clusters across all the sites.

They support any tape library and any NFS, SMB, S3 orAzure compatible object or file storage. Stronglink can move or copy data from one tier/cluster to another based on policies AND the end-users never sees any difference in their workflow or mount/share points.

One challenge with typical tape archives is that they can make use of proprietary tape data formats which are not accessible outside those systems. StrongLink has gone with a completely open-source, LTFS file format on tape, which is well documented and is available to anyone.

Floyd also made it a point of saying they don’t use any stubs, or soft links to provide their data placement magic. They only use standard file metadata.

File data moves across the hierarchy based on policies or by request. One of the secrets to StrongLink success is all the work they have done to ensure that any data movement can occur at line rate speeds. They heavily parallelize any data movement that’s required to support data placement across as many servers as the customer wants to throw at it. StrongBox services will help right-size the customer deployment to support any data movement performance that is required.

StrongLink supports up to 3-way replication of a customer’s data archives. This supports a primary archive and 3 more replicas of data.

Floyd mentioned a couple of big customers:

  • One autonomous automobile supplier, was downloading 2PB of data from cars in the field, processing this data and then moving it off their servers to get ready for the next day’s data load.
  • Another weather science research organization, had 150PB of data in an old tape archive and they brought in StrongLink to migrate all this data off and onto LTFS tape format as well as support their research activities which entail staging a significant chunk of file data on research servers to do a climate run/simulation.

NASA, another StrongLink customer, operates slightly differently than the above, in that they have integrated StrongLink functionality directly into their applications by making use of StrongBox’s API.

StrongLink can work in three ways.

  • Using normal file access services where StrongLink virtualizes your NFS, SMB, S3 or Posix file environment. For this service StrongLink is in the data path and you can use policy based management to have data moved or staged as the need arises.
  • Using StrongLink CLI to move or copy data from one tier to another. Many HPC customers use this approach through SLURM scripts or other orchestration solutions.
  • Using StrongLink API to move or copy data from one tier to another. This requires application changes to take advantage of data placement.

StrongBox customers can of course, use all three modes of operation, at the same time for their StrongLink data galaxy. StrongLink is billed by CPU/vCPU level and not for the amount of data customers throw into the archive. This has the effect of Customers gaining a flat expense cost, once StrongLink is deployed, at least until they decide to modify their server configuration.

Floyd Christofferson, CEO StrongBox Data Solutions

As a professional involved in content management and storage workflows for over 25 years, Floyd has focused on methods and technologies needed to manage massive volumes of data across many different storage types and use cases.

Prior to joining SBDS, Floyd worked with software and hardware companies in this space, including over 10 years at SGI, where he managed storage and data management products. In that role, he was part of the team that provided solutions used in some of the largest data environments around the world.

Floyd’s background includes work at CBS Television Distribution, where he helped implement file-based content management and syndicated content distribution strategies, and Pathfire (now ExtremeReach), where he led the team that developed and implemented a satellite-based IP-multicast content distribution platform that manages delivery of syndicated content to nearly 1,000 TV stations throughout the US.

Earlier in his career, he ran Potomac Television, a news syndication and production service in Washington DC, and Manhattan Center Studios, an audio, video, graphics, and performance facility in New York.

117: GreyBeards talk HPC file systems with Frank Herold, CEO of ThinkParQ, makers of BeeGFS

We return back to our storage thread with a discussion of HPC file systems with Frank Herold, (@BeeGFS) CEO of ThinkParQ GmbH, the makers of BeeGFS. I’ve seen BeeGFS start to show up in some IO500 top storage benchmark results and as more and more data keeps coming online every day, we thought it time to start finding out how our friends in the HPC world handle their data deluge.

Frank’s a former rocket scientist, that’s been in and around the storage industry for years, and was very knowledgeable about BeeGFS’s software defined, parallel file system. He seemed to have a great grasp of the IO requirements in HPC, Life Sciences and other HPC-like applications. Listen to the podcast to learn more.

Turns out that ThinkParQ is a spinoff of the research institute in Germany that originally developed BeeGFS parallel file system. There are apparently two version of their product one which is publicly available (downloadable from their website) and another with commercial support. It’s not quite 100% open source but it’s got a lot of open source in it and their GIT repository is available

BeeGFS was primarily focused on HPC workloads but as this type of work has become more mainstream, they have moved beyond HPC and now have significant installations in Life Sciences, Oil&Gas and many other big data environments.

It runs on x86/AMD, OpenPower, and ARM CPUs. BeeGFS comes as a number of services, one of which is a storage service which uses a backend with ZFS or XFS file system. It also uses (POSIX compliant) host client software to access their system. There’s also a metadata and monitoring service. Most of the time these services run on separate servers but BeeGFS also supports a “converged mode”, where all these services run on a single server. And you can have multiple converged mode servers in a cluster.

BeeGFS is a parallel file system. This means that it intrinsically supports multiple metadata services/servers and multiple storage servers which allow it to scale up storage bandwidth and performance considerably beyond single appliance systems. Data is automatically distributed across all the storage servers in the configuration, unless you specify that data reside on specific, say all flash storage servers. Similarly, metadata is automatically distributed across all metadata servers in the system.

They don’t support any specific RAID protection other than mirroring and that really to speed up read throughput. Rather they depend on the underlying XFS/ZFS file system to provide drive failure protection (RAID5/6).

One of BeeGFS’s selling points is that it has few tuning parameters that a customer needs to fiddle with. Frank said it runs quite well right out of the box.

BeeGFS offers a single name space that spans the cluster (of metadata servers/storage servers). But customers can elect to split this name space across a subset of these metadata and storage servers, and by doing so they create multiple BeeGFS clusters.

There’s no inherent support for NFS or SMB but customers can configure NFS or SAMBA servers that use BeeGFS as backend storage. Also, there’s no data reduction built into BeeGFS and no automatic data tiering across the backend storage (file systems).

But as noted above, customers can direct which backend storage to use to hold their data. And they do offer a CLI data movement primitive and customers can use this in conjunction with other software to implement storage tiering or do it themselves.

Metadata performance is extremely important for small files and for large multi Billion object file systems. BeeGFS uses extensive metadata caching to provide faster access to this information.

Speaking of small file performance, we had a decent discussion on the tradeoffs involved between small and large file performance. And although BeeGFS has decent small file performance it’s not a be all for every small file intensive application. According to Frank, not every small file workload is optimal for BeeGFS.

They offer BeeOND which is BeeGFS on demand. This is an integration with Slurm workload scheduler (HPC work scheduler) that allows customers to spin up a scratch BeeGFS parallel file system across compute servers with storage.

Slurm’s BeeOND integration brings all BeeGFS services up and deploys them on compute nodes you specify. At this point you have a fully installed BeeGFS (scratch) parallel file system. Customers may use this scratch file system to support any compute-data intensive workload theyneed to run. When no longer needed, Slurm can be directed to automatically dismantle the BeeGFSl file system.

We talked about BeeGFS partners. They have a number of regional partners that provide installation and onsite support and a number of technical partners, such as NetApp, Dell, HPE and INSPUR, that supply BeeGFS configured servers and systems for deployment/installation.

Frank Herold, CEO ThinkparQ

Frank Herold is the CEO of ThinkParQ GmbH – the company behind BeeGFS. He actively leads the company and the product strategy of BeeGFS as a global player for parallel high-performance file systems.

Prior to joining ThinkParQ, he held various senior management positions within ADIC and Quantum Corporation, responsible for market segments within the academic and scientific research, oil and gas, broadcast and video surveillance sectors, focusing on large scale, high-performance and enterprise accounts within EMEA. 

Frank has over 25 years of experience in the IT industry and holds a master’s degree in engineering (Dipl. -Ing.) in rocket science.

113: GreyBeards talk storage for next gen. workloads with Liran Zvibel, Co-Founder & CEO WekaIO

Sponsored By:

I’ve known Liran Zvibel, Co-founder and CEO of Weka IO for many years now and it’s the second time he’s been on our show, (see: Episode 56: GreyBeards talk high performance file storage...). In those days, WekaIO was just coming out and hitting the world with this extremely high-performing, scale out unstructured data solution. Well since then, they’ve just gotten better.

Keith and I had a great time talking with Liran again. Liran has deep knowledge about unstructured data and how enterprises use it these days. WekaIO’s story, over the last two years has gone beyond great performance to real world, hybrid cloud offerings e as well as going after the cloud native app’s (read Kubernetes [K8S]) persistent storage. Listen to the podcast to learn more.

We started with a history lesson on WekaIO. Back in those days (which persists today, I might add) there were many IO workloads that required companies to purchase different solutions for different work. For example, they needed DAS or SAN for performance, NAS for ease of access and object for scale. WekaIO came out with an answer to all these problems in a single, scaleable storage system. That is, they performed IO as fast as DAS or SAN block, had all the ease of access of NAS, and could scale as much as object.

However, the real culprit holding the world back was “NFS”. At the outset NFS was designed (back in the 1990s) with the then current networking speeds available (10-100Mbps), which performed just fine at those speeds. But when 10-100GbE came out in the 2000’s, NFS’s metadata overhead was too chatty to support wire speeds. Thus, any storage that depended on NFS protocols couldn’t supply (small) files fast enough for modern applications.

This is why WekaIO has moved to not only support NFS and SMB but also POSIX and NVIDIA® GPUDirect® Storage interfaces. By offering POSIX, WekaIO is able to plug into standard Linux and Windows server systems and provide excellent small file performance. Of course applications that demand small file performance today are mostly data analytics and AI/ML/DL workloads.

Consequently., NVIDIA came out with their GPUDirect Storage protocol to address getting small file (data) into GPUs faster. With GPUDirect, storage systems can RDMA data directly from storage to GPU memory and vice versa, with no OS intervention (other than to set up the transfer). If you happen to have a small file, high performing storage system attached to your fabric that supports GPUDirect , like WekaIO, you can significantly speed up your AI/ML/DL workloads.

Next we started talking K8S storage. WekaIO usestheir POSIX interface in their CSI plugin to support K8S container persistent storage. Again, supplying high performance for small files seems to be tailor made for K8S container applications that exist today and will for the foreseeable future.

Enter the cloud. Almong other things, WekaIO is a AWS primary storage vendor. It also offers snap to cloud. And with both of these in tandem, it’s just become a lot easier to move and access your unstructured data in the cloud. Liran mentioned that WekaIO primary storage in AWS operates across AZ’s. This means it can be configured to support better availability than EBS.

Large BioPharma companies are using WekaIO in AWS to store and process field data and research data, so that this work can be done around the world. Some companies have run out of compute in a single AZ (unbelievable I know but it’s COVID). By offering multi-AZ support unstructured data access with WekaIO, these companies can spread their compute across AZ’s and region and still access their data. And when their products are ready for gov’t certification, having all this data in the cloud, can make provide an easy way to have gov’t access this same data.

Liran Zvibel, Co-founder and CEO WekaIO

As Co-Founder and CEO, Mr. Liran Zvibel guides long term vision and strategy at WekaIO. Prior to creating the opportunity at WekaIO, he ran engineering at social startup and Fortune 100 organizations including Fusic, where he managed product definition, design, and development for a portfolio of rich social media applications.

Liran also held principal architectural responsibilities for the hardware platform, clustering infrastructure and overall systems integration for XIV Storage System, acquired by IBM in 2007.

Mr. Zvibel holds a BSc.in Mathematics and Computer Science from Tel Aviv University.

108: GreyBeards talk DNA storage with David Turek, CTO, Catalog DNA

The Greybeards get off the beaten (enterprise) path this month, to see what lies ahead with a discussion on DNA storage. David Turek, CTO, Catalog DNA (@CatalogDNA) is a long time IBMer that had been focused on HPC systems at IBM but left and went to Catalog DNA to pursue the commercialization of DNA storage, an “emerging” technology. CatalogDNA is a company out of Boston that had recently closed a round of funding and are focused on bringing DNA storage out into the world of IT.

David was a pleasure to talk and has lot’s of knowledge on HPC and enterprise data center solutions. He also has a good grasp of what it will take to bring DNA storage to market. Keith has had some prior experience with DNA technologies in BioPharma so could talk in more detail about the technology and its ecosystem. [We’re trying out a new format, let us know what you think; The Eds.]

Ray has written about DNA storage in his RayOnStorage Blog, most recently in April of this year and May of last year. It’s been an ongoing blog topic of his for almost a decade now. When Ray was interviewed about the technology he thought it interesting but had serious obstacles with read and write latencies and throughput as well as the size of the storage device.

Well CatalogDNA seems to have got a good handle on write throughput and are seriously working on the rest.

However, DNA storage’- volumetric density was always of exceptional. Early on in the podcast, David mentioned that DNA storage was 6 orders of magnitude (1 million times) more dense in bytes/mm**3 than magnetic tape today. An LTO8 tape device stores 12TB (uncompressed) in a tape cartridge, 14.2 in**3 (230.3 cm**3) or roughly 845GB/in**3 (52GB/cm**3). One million times this, would be 12EB in the same volume.

The challenge with LTO8, disk or SSD storage today is at some point you have to move the data from one device to a more modern device. This could be every 3-5 years (for disk or SSD) or 25-30 years for tape. In either case, at some point IT would need to incur the cost and time to move the data. Not much of a problem for 100TB or so but when you start talking PB or EB of data, it can be a never ending task.

DNA storage

David mentioned Catalog uses “synthetic DNA” in their storage. This means the DNA it uses is designed to be incompatible with natural DNA such that it wouldn’t work in a cell. It has stops or other biological mechanisms to inhibit it’s use in nature. Yes it uses the same sugars, backbones, and other chemistry of biologically active DNA, but it has been specifically modified to inhibit its use by normal cellular machinery. 

DNA storage has a number of unique capabilities :

  • It can be made to last forever, by being dried out (dessicated) and encased in a crystal and takes 0 power/energy to be stored for eons.
  • It can be cheaply and easily replicated, almost an infinite number of times, for only the cost of chemical feedstock, chemical interactions and energy. Yes, this may take time but the process scales up nicely. One could make 2 copies in first cycle, 4 in the 2nd, 8 in the 3rd, etc and by doing this it would only take 20 cycles to create a million copies. If each cycle takes 10 minutes, in 3:20, you could have a million copies of 1EB of data.
  • It can be easily searched for target information. This involves fabricating a DNA search molecule and inserting it into the storage solution. Once there it would match up with the DNA segment that held your key. And of course, the search molecule and the data could be replicated to speed up any search process.
  • We already mentioned the extreme density advantage above.

Speed of DNA storage access

David said they can already write Catalog DNA storage in MB/sec.

The process they use to write is like a conveyer belt which starts off with a polyethylene sheet (web actually). Somewhere, the digital data comes in, is chunked and transformed into DNA strand (25-50 base pairs) molecules or dots. The polyethylene sheet rolls into a machine that uses multiple 3D print heads to deposit dots (the DNA strand data chunks) at web points. This machine/process deposits 100K or more of these dots onto the web. The sheet then moves to the next stage where the DNA molecules are scraped off and drained into a solution. Then a wet process occurs which uses chemistry to make the DNA more readable and enables the separate DNA molecules to connect into a data strand. Then this data strand goes into another process where it gets reduced in volume and so that it is more stable.

If needed, one can add another step that dries out or desiccates the data strand into even a smaller volume which can then be embedded into a crystalline structure which could last for centuries.

David compared the DNA molecules (data chunks) to Legos, only they are the same pieces in a million different colors Each piece represents some segment of data bits/bytes. Using chemistry and proprietary IP each separate DNA molecule self organizes (connects) into a data strand, representing the information you want to store.

Reading DNA involves, off the shelf, DNA sequencers. The one Catalog currently uses is the Oxford NanoPore device, but there are others. David didn’t say how fast they could read DNA data. But current DNA reading devices destroy the data. So making replicas of the data would be required to read it.

David said their current write device is L shaped with one leg about 14’ (4.3m) long and the other about 12’ (3.7m) long with each leg being about 3’ (0.9m) wide.

Searching EB of data in minutes?!

DNA strands can be searched (matched) using a search molecule and inserting this into the storage solution (that holds the data strands). Such a molecule will find a place in the data that has a matching (DNA) data element and I believe attach itself to the data strand.

For example, lets say you had recorded all of a country’s emails for a month or so and you wanted to search them for the words, “bomb”, “terrorist”, “kill”, etc. One could create a set of search molecules, replicate them any number of times (depending on how quickly you wanted to search the data and how many matches you expected), and insert them into a data pool with multiple data strands that stored the email traffic.

After some time, you’d come back and your search would be done. You’d need to then extract the search hits, and read out the portion of the data strands (emails) that matched. I’m guessing extraction would involve some sort of (wet) chemical process or filtration.

State of Catalog DNA storage

David mentioned that as a publicity stunt they wrote the whole Wikipedia onto Catalog DNA storage. The whole Wikipedia fit into a cylinder about the height of a big knuckle on your hand and in a width smaller than a finger. The size of the whole Wikipedia, with complete edit history is 10TB uncompressed and if they stored all the edit versions plus its media such as images, videos, audio and other graphics, that would add another 23TB (as of end of 2014), so ~33TB uncompressed.

David believes in 18 months they could have a WORM (write once, read many times) data storage solution that could be deployed in customer data centers which would supply immense data repositories in relatively small solution containers.

CatalogDNA is currently in a number of PoCs with major corporations (not labs or universities) to show how DNA storage technology can be used to solve problems.

David believes that at some point they will be able to make compute engines entirely of DNA. At that point, one could have a combined compute and storage (HCI-like) DNA server using the same technology in a solution. And as mentioned previously, one could replicate from one DNA server & storage to a million DNA servers & storage in just 20 cycles. How’s that for scale out.


David Turek, CTO Catalog DNA

Dave Turek is Catalog’s Chief Technology Officer. He comes to Catalog from IBM where he held numerous executive positions in High Performance Computing and emerging technologies.

He was the development executive for the IBM SP program which produced the first commercially successful massively parallel system; he started IBM’s Linux Cluster business; launched an early offering in Cloud computing called Deep Computing Capacity on Demand; produced the Roadrunner system, the world’s first petascale computer; and was responsible for IBM’s exascale strategy which led to the deployment of the Summit and Sierra systems at Oak Ridge and Lawrence Livermore National Laboratories respectively.

David has been invited to testify to Congress on numerous occasions regarding the future of computing in the US and has helped establish technical collaborations with universities, businesses, and government agencies around the world.

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106: Greybeards talk Intel’s new HPC file system with Kelsey Prantis, Senior Software Eng. Manager, Intel

We had talked with Intel at Storage Field Day 20 (SFD20), about a month ago. At the virtual event, Intel’s focus was on their Optane PMEM (persistent memory) technology. Kelsey Prantis (@kelseyprantis), Senior Software Engineering Manager, Intel was on the show and gave an introduction into Intel’s DAOS (Distributed Architecture Object Storage, DAOS.io) a new HPC (high performance computing, super computers) file system they developed from scratch to use leading edge, Intel technologies, and Optane PMEM was one of them.

Kelsey has worked on LUSTRE and other HPC file systems for a long time now and came into the company from the acquisition of Whamcloud. Currently, she manages the development team working on DAOS. DAOS is a new HPC object storage file system which is completely open source (available on GitHub).

DAOS was designed from the start to take advantage of NVMe SSDs and Optane PMEM. With PMEM, current servers can support up to 20TB of memory. Besides the large memory sizes, Optane PMEM also offers non-volatile memory and byte addressability (just like DRAM). These two characteristics opens up new functionality that allows DAOS to move beyond legacy, block oriented, storage architectures that have been the only storage solution for HPC (and the enterprise) for decades now.

What’s different about DAOS

DAOS uses PMEM for all metadata and for storing small files. HPC IO has always focused on heavy bandwidth (IO using large blocks) oriented but lately newer applications have emerged, such as AI/ML/DL, data analytics and others, that use smaller files/blocks. Indeed, most new HPC clusters and supercomputers are deploying almost as many GPUs as CPUs in their configurations to support AI activities.

The problem is that these newer applications typically consume much smaller files. Matt mentioned one HPC client he worked with was processing small batches of seismic data, to predict, in real time, earthquakes that were happening around the world.

By using PMEM for metadata and small files, DAOS can be much more responsive to file requests (open, close, delete, status) as well as provide higher performing IO for small files. All this leads to a much better performing system for the new HPC workloads as well as great sustainable performance for the more traditional large file workloads.

DAOS storage

DAOS provides a cluster storage system that can be configured with from 1 (no data protection), but more normally 3 nodes (with data protection) at a minimum to 512 nodes (lab tested). Data protection in DAOS is currently based on mirroring data and can use from 0 to the number of nodes in a cluster as data mirrors.

DAOS system nodes are homogeneous. That is they all come with the same amount of PMEM and NVMe SSDs. Note, DAOS doesn’t support disk drives. Kelsey mentioned DAOS node hardware can be tailored to suit any particular application environment. But they typically require an average of 6% of overall DAOS system capacity in PMEM for metadata and small file activity.

DAOS current supports their own API, POSIX, HDFS5, MPIIO and Apache Spark storage protocols. Kelsey mentioned that standard POSIX uses a pessimistic conflict resolution mode which leads to performance bottlenecks during parallel access. In contrast, DAOS’s versos of POSIX uses optimistic conflict resolution, which means DAOS starts writes assuming there’s no conflict, but if one occurs it handles the conflict in real time. Of course with all the metadata byte addressable and in PMEM this doesn’t take up a lot of (IO) time.

As mentioned earlier, DAOS data protection uses mirror-replicas. However, unlike most other major file systems, DAOS mirroring can be done at the object level. DAOS internally is an object store. Data organization on DAOS starts at the pool level, underneath that is data containers, and then under that are objects. Any object in DAOS can have its own mirroring configuration. DAOS is working towards supporting Erasure Coding as another form of data protection for a future release.

DAOS performance

There’s a new storage benchmark that was developed specifically for HPC, called the IO500. The IO500 benchmark simulates a number of different HPC workloads, measures performance for each of them, and computes an (aggregate) performance score to rank HPC storage systems.

IO500 ranks system performance using two lists: one is for any sized configuration that typically range from 50 to 1000s of nodes and their other list limits the configuration to 10 nodes. The first performance ranking can sometimes be gamed by throwing more hardware into a cluster. The 10 node rankings are much harder to game this way and from our perspective, show a fairer comparison of system performance.

As presented (virtually) at ISC 2020, DAOS took the top spot on the IO500 any size configuration list and performed better than 2X the next best solution. And on the IO500 10 node list, Intel’s DAOS configuration, Texas Advanced Computing (TAC) DAOS configuration, and Argonne Nat Labs DAOS configuration took the top 3 spots and had 3X better performance than the next best, non-DAOS storage system.

The Argonne National Labs has already stated that they will be using DAOS in their new HPC system to be deployed in the near future. Early specifications for storage at the new Argonne Lab required support for 230PB of data and 25TB/sec of bandwidth.

The podcast ran ~43 minutes. Kelsey was great to talk with and very knowledgeable about HPC systems and HPC IO in particular. Matt has worked at Argonne in the past so understood these systems better than I. Sadly, we lost Matt’s end of the conversation about 1/2 way into the recording. Both Matt and I thought that DAOS represents the birth of a new generation of HPC storage. Listen to the podcast to learn more.


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Kelsey Prantis, Senior Software Engineering Manager, Intel

 Kelsey Prantis heads the Extreme Storage Architecture and Development division at Intel Corporation. She leads the development of Distributed Asynchronous Object Storage (DAOS), an open-source, low-latency and high IOPS object store designed from the ground up for massively distributed Non-Volatile Memory (NVM).

She joined Intel in 2012 with the acquisition of Whamcloud, where she led the development of the Intel Manager for Lustre* product.

Prior to Whamcloud, she was a software developer at personal genomics and biotechnology company 23andMe.

Prantis holds a Bachelor’s degree in Computer Science from Rochester Institute of Technology