“The long run is already right here,” science fiction author William Gibson as soon as stated. “It’s simply not evenly distributed but.” One one who’s seeking to deliver information storage into the longer term and make it broadly distributed is David Flynn, who’s the CEO and founding father of Hammerspace in addition to a BigDATAwire Individual to Look ahead to 2025.
Even earlier than founding Hammerspace in 2018, Flynn had an eventful profession in IT, together with growing solid-state information storage platforms at Fusion-iO and dealing with Linux-based HPC methods. However now as Hammerspace features traction, Flynn is raring to construct the following era of distributed file methods and hopefully remedy a few of the hardest information issues on the earth.
Right here’s our latest dialog with Flynn:
BigDATAwire: First, congratulations in your choice as a 2025 BigDATAwire Individual to Watch! Earlier than Hammerspace, you have been the CEO and founding father of Fusion-io, which SanDisk purchased in 2014. Earlier than that, you have been chief architect at Linux Networx, the place you designed a number of of the world’s largest supercomputers. How did these experiences lead you to discovered Hammerspace in 2018?
David Flynn: It’s a very attention-grabbing trajectory, I believe, that led to the creation of Hammerspace. Early on in my profession, I used to be embedding alternate open-source software program like Linux into tiny methods like TV set-top packing containers, company good terminals and the like. After which I got here to design lots of the world’s largest supercomputers within the high-performance computing trade that leveraged applied sciences like Linux clustering, InfiniBand, RDMA-based applied sciences.
These two extremes – small embedded methods versus huge supercomputers – won’t appear to have a ton in frequent, however they share the necessity to extract absolutely the most efficiency from the {hardware}.
This led to the creation of Fusion-io, which pioneered the usage of flash for enterprise software acceleration, which till that time was typically used for embedded methods in shopper electronics — for instance, the flash on units like iPods and early cell telephones. I noticed a possibility to take that innovation from the buyer electronics world and translate into the information middle, which created a shift away from mechanical arduous drives in direction of solid-state storage. The difficulty then turned that this transition in direction of solid-state drives wanted extraordinarily quick efficiency; the information wanted to be bodily distributed throughout a set of servers or throughout third get together storage methods.
(ALPAL-images/Shutterstock)
The introduction of ultra-high-performance flash was instrumental in addressing this problem of decentralized information, and abstracting information from the underlying infrastructure. Most information in enterprises at this time is unstructured, and it’s arduous for these organizations to seek out and extract the worth inside it.
This realization finally led to the creation of Hammerspace, with the imaginative and prescient to make all enterprise information globally accessible, helpful, and indispensable, utterly eliminating information entry delays for AI and high-performance computing.
BDW: We’re 20 years into the Huge Knowledge increase now, but it surely feels as if we’re at an inflection level in relation to storage. What do you see as the primary drivers this time round, and the way is Hammerspace positioned to capitalize on them?
DF: To actually thrive on this subsequent information cycle, we’ve acquired to repair the damaged relationship between the information and the information infrastructure the place it’s saved. Enterprises have to assume past storage and moderately how they’ll rework information entry and administration in fashionable AI environments.
Distributors are all competing to supply the efficiency and scale that’s wanted to help AI workloads. Besides it’s not nearly accelerating information throughput to GPU servers – the core drawback is that information pathways between exterior storage and GPU servers get bottlenecked by pointless and inefficient hops within the information path throughout the server node and on the community, whatever the exterior shared storage in use.
The answer right here, which is addressed by Hammerspace’s Tier 0, is using the native NVMe storage which is already included inside GPU servers to speed up AI workloads and enhance GPU utilization. By leveraging the present infrastructure and built-in Linux capabilities, we’re eradicating that bottleneck with out including complexity.
We do that by leveraging the intelligence that’s constructed into the Linux kernel which permits our clients to make the most of the present storage infrastructure they’re already utilizing, with out proprietary shopper software program or different level options. That is along with offering international multi-protocol file/object entry, information orchestration, information safety, and information providers throughout a world namespace.
BDW: You acknowledged on the HPC + AI on Wall Avenue 2023 occasion that we have been all duped by S3 and object storage to surrender the advantages of native entry inherent with NFS. Isn’t the struggle in opposition to S3 and object storage destined to fail, or do you see a resurgence in NFS?
(whiteMocca/Shutterstock)
DF: Let’s be clear—its not about object or file, nor, S3 or NFS. Storage interfaces wanted to evolve to perform scale. S3 happened and have become the default for cloud-scale storage for cause: older variations of NFS merely couldn’t scale or carry out on the ranges wanted for early HPC and AI workloads.
However that was then. Right this moment, NFSv4.2 with pNFS is a special animal—totally matured, built-in into the Linux kernel, and able to delivering huge scale and native efficiency with out proprietary shoppers or complicated overhead. In reality, it’s turn out to be a regular for organizations that demand excessive efficiency and environment friendly entry throughout giant, distributed environments.
So this isn’t about selecting sides in a file vs. object debate. That framing is outdated. The actual breakthrough is enabling each file and object entry inside a single, standards-based information platform—the place information might be orchestrated, accessed natively, and served by means of whichever interface a given software or AI mannequin requires.
S3 isn’t going away—many apps are written for it. Nevertheless it’s not the one possibility for scalable information entry. With the rise of clever information orchestration, Tier 0 storage, and fashionable file protocols like pNFS, we will now ship efficiency and suppleness with out forcing a alternative between paradigms.
The long run isn’t about combating S3—it’s about transcending the boundaries of each file and object storage with a unified information layer that speaks each languages natively, and places the information the place it must be, when it must be there.
BDW: How do you see the AI revolution of the 2020s impacting the earlier decade’s massive advance, which was separating compute and storage? Can we afford to deliver massive GPU compute to the information, or are we destined to return to transferring information to compute?
DF: The separation of compute and storage made sense when bandwidth was low cost, workloads have been batch-oriented, and efficiency wasn’t tied to GPU utilization. However within the AI period, the place idle GPUs imply wasted {dollars} and misplaced alternatives, that mannequin is beginning to crack.
The problem now isn’t nearly the place the compute or information lives—it’s about how briskly and intelligently you’ll be able to bridge the 2. At Hammerspace, we consider the reply is to not return to previous habits, however to evolve past inflexible infrastructure with a world, clever information layer.
We make all information seen and accessible in a world file system—irrespective of the place it bodily resides. Whether or not your software speaks S3, SMB, or NFS (together with fashionable pNFS), the information seems native. And that’s the place the magic occurs: our metadata-driven orchestration engine can transfer information with excessive granularity—file by file—to the place the compute is, with out disrupting entry or requiring rewrites.
So the true reply isn’t selecting between transferring compute to information or vice versa. The actual reply is dynamic, policy-driven orchestration that locations information precisely the place it must be, simply in time, throughout any storage infrastructure, so AI and HPC workloads keep fed, quick, and environment friendly.
The AI revolution doesn’t undo the separation of compute and storage—it calls for we unify them with orchestration that’s smarter than both alone.
BDW: What are you able to inform us about your self outdoors of the skilled sphere – distinctive hobbies, favourite locations, and many others.? Is there something about you that your colleagues may be shocked to be taught?
DF: Outdoors of labor, I spend as a lot time as I can with my youngsters and household—often on skis or filth bikes. There’s nothing higher than getting out on a mountain or a path and simply having fun with the journey. It’s quick, technical, and just a little chaotic—just about my perfect weekend.
That stated, I’ve by no means actually separated work from play within the conventional sense. For me, writing software program and inventing new methods to unravel robust issues is what I’ve at all times cherished to do. I’ve been constructing methods since I used to be a child, and that curiosity by no means actually went away. Even once I’m off the clock, I’m usually deep in code or sketching out the following thought.
Folks may be shocked to be taught that I genuinely benefit from the artistic course of behind tech—whether or not that’s low-level system design or rethinking how infrastructure ought to work within the AI period. Some people unwind with hobbies. I unwind by fixing arduous issues.
You possibly can learn the remainder of our conversations with BigDATAwire Folks to Watch 2025 honorees right here.
