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Application-oriented testing also showed VAST performing strongly against Lustre-based environments, with CSCS concluding that “ VAST Data can be used as encrypted scratch for HPC\u002FAI.”",{"data":12299,"content":12300,"nodeType":12275},{},[12301],{"data":12302,"content":12303,"nodeType":1920},{},[12304,12308,12313],{"data":12305,"marks":12306,"value":12307,"nodeType":1906},{},[],"VAST was built for where infrastructure is going,” said ",{"data":12309,"marks":12310,"value":12312,"nodeType":1906},{},[12311],{"type":12164},"Christopher Huggins, Managing Director of AI & HPC, EMEA at VAST Data",{"data":12314,"marks":12315,"value":12316,"nodeType":1906},{},[],". “As HPC and AI converge, the most advanced supercomputing environments are being asked to support sensitive data, secure multi-tenancy, cloud-like services, new I\u002FO patterns and governance at extreme scale. 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The VAST AI OS consolidates foundational data and compute services and agentic execution into one scalable platform, enabling organizations to deploy and facilitate communication between AI agents, reason over real-time data, and automate complex workflows at global scale. Built on VAST’s breakthrough DASE architecture – the world’s first true parallel distributed system architecture that eliminates tradeoffs between performance, scale, simplicity, and resilience – VAST has transformed its modern infrastructure into a global fabric for reasoning AI. 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supercomputer",{"metadata":12781,"sys":12784,"fields":12792},{"tags":12782,"concepts":12783},[],[],{"space":12785,"id":12787,"type":933,"createdAt":12788,"updatedAt":12789,"environment":12790,"publishedVersion":691,"revision":900,"locale":672},{"sys":12786},{"type":656,"linkType":657,"id":658},"vy8auaGQn6X4iHhftvVlr","2026-07-01T13:03:29.474Z","2026-07-01T13:04:35.415Z",{"sys":12791},{"id":664,"type":656,"linkType":665},{"title":12793,"description":2030,"file":12794},"Alps Supercomputer",{"url":12795,"details":12796,"fileName":12800,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002Fvy8auaGQn6X4iHhftvVlr\u002F0c8e7e5edc35d681d130b68faee84d86\u002FScaling_Alps_for_AI__HPC__and_Trusted_Research_Environments-Web.jpg",{"size":12797,"image":12798},189283,{"width":6717,"height":12799},720,"Scaling Alps for AI, HPC, and Trusted Research Environments-Web.jpg","The harder challenge is building infrastructure flexible enough to manage, secure and serve the unprecedented volumes of data that modern AI-driven science will continue to generate.","More processors, faster interconnects, and ever-larger GPU clusters have defined each new generation of supercomputing systems over the last two decades. But that equation is changing as AI becomes a permanent part of scientific computing. The problem is shifting (again) toward something far more complicated, which is building infrastructure that can simultaneously support traditional HPC, large-scale AI training, inference, and increasingly sensitive data.\n\nThat shift is evident in the design of Alps, the flagship supercomputer at the Swiss National Supercomputing Centre (CSCS).\n\nDuring a recent episode of the Shared Everything podcast, Maxime Martinasso, head of engineering at CSCS, described how the center deliberately abandoned the traditional idea of a single supercomputer environment in favor of infrastructure that can be assembled differently for each scientific community.\n\n\u003Ciframe height=\"200px\" width=\"100%\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https:\u002F\u002Fplayer.simplecast.com\u002F4c2b4c54-3797-48f3-91d8-25b1e4c184a3?dark=false\">\u003C\u002Fiframe>\n\n\"We started to look at reversing this equation,\" Martinasso said. \"Not having the user adapt to the machine, but having the machine adapt to the user and science.\"\n\nThat philosophy touches nearly every layer of Alps. Instead of treating compute nodes, storage systems and networking as fixed infrastructure, CSCS designed the machine so resources can be composed into dedicated platforms with their own software stacks, schedulers and services.\n\nMaterials science researchers receive an environment optimized for high-throughput computing. Climate scientists use different scheduling policies for massive simulation campaigns. AI researchers have separate environments for training large models and serving inference workloads, including Kubernetes deployed directly onto compute nodes.\n\nThe hardware itself reflects the same philosophy. Alps combines NVIDIA Grace Hopper systems, AMD MI300A processors, earlier-generation GPUs, CPU-only nodes and multiple storage technologies, allowing CSCS to match infrastructure to applications instead of forcing every workload into the same configuration.\n\n\"We can combine both the hardware you need and the software for science,\" Martinasso explained.\n\nHe emphasizes that flexibility extends well beyond compute.Like most large HPC centers, CSCS spent years relying on Lustre as its primary parallel file system. Performance was never the only consideration, however. As researchers began bringing new AI workloads and increasingly diverse requirements, the storage layer itself became a limiting factor.\n\n\"Lustre has these issues,\" Martinasso said. \"For us the particularity is a lack of features.\"\nThose features had less to do with raw bandwidth than operational capabilities. Different research communities required different approaches to data management, multi-tenancy, encryption and security. Rather than searching only for a faster file system, CSCS was looking for infrastructure that could support fundamentally different operating models.\n\n\"We were looking for a solution with a richer set of features,\" he said. Initially, replacing Lustre was never the goal. CSCS simply wanted additional capabilities while maintaining performance. Early benchmarking looked promising, but real scientific applications initially failed to match Lustre's performance.\n\nRather than abandoning the effort, engineers from both organizations spent months profiling workloads, tuning software and adjusting I\u002FO behavior until application performance matched what researchers expected.\n\n\"What was very surprising,\" Martinasso said, \"is that by doing deep technical work and finding the right combination of parameters, we ended up having actually the same performance.\"\n\nThat work eventually allowed CSCS to begin evaluating the platform as a scratch file system for production scientific workloads while retaining capabilities that traditional HPC storage had not previously provided.\n\nThose capabilities became particularly important in a second project that Martinasso believes represents one of the most important directions for national supercomputing centers.\n\nHealthcare organizations increasingly want access to AI infrastructure for personalized medicine, but they cannot simply upload sensitive patient information into a shared supercomputing environment. Regulatory requirements, liability and public trust make that impossible.\n\n\"The potential is so high,\" Martinasso said. \"But you need the data, and this data is confidential.\"\n\nInstead of becoming the controller of hospital data, CSCS designed a trusted research environment that effectively hands a secure portion of Alps over to participating institutions. Individual partitions are isolated from the rest of the system. Hospital administrators control encryption keys. CSCS provides compute capacity while deliberately preventing itself from accessing protected information.\n\n\"We should not be the data controller,\" Martinasso explained. \"We just enable the capability to process the data.\"\n\nThat distinction fundamentally changes responsibility. If hospitals control encryption keys stored outside the supercomputing center, they retain ownership of their information while still accessing world-class AI infrastructure. Should those keys ever be destroyed, the stored information becomes unreadable. \"If something happens, they just destroy the keys,\" Martinasso said. \"What we have is encrypted data that cannot be decrypted anymore.\"\n\nWithout capabilities such as multi-tenancy and external key management, Martinasso believes the alternative would have required physically separate infrastructure for every participating institution.\n\n\"If we didn't have multi-tenancy, then we would have to procure systems per hospital,\" he said. \"There is no way around it.\"\n\nThe discussion ultimately returned to what may become the next major infrastructure challenge facing HPC.\n\nTraining foundation models consumes enormous computational resources, but scientific inference may ultimately generate even larger storage problems. Weather forecasting, scientific simulations and other AI-assisted research can produce terabytes of output in minutes. Managing, classifying and protecting those datasets could become as significant an engineering challenge as building larger GPU clusters.\n\n\"When you start to do science,\" Martinasso said, \"suddenly you can generate huge amounts of data... 10 terabytes of data per minute.\"\n\nThat explosion creates demands not only for storage capacity but for resilience, security, lifecycle management and efficient sharing across research communities. AI may have increased demand for compute, but its long-term effect may be to elevate data infrastructure from supporting technology to primary infrastructure.\n\nFor much of HPC's history, storage was expected to keep up with compute. Increasingly, compute may become the easy part. The harder challenge is building infrastructure flexible enough to manage, secure and serve the unprecedented volumes of data that modern AI-driven science will continue to 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Straight Lines Can't Cross AI’s Data Chaos","hpcs-straight-lines-cant-cross-ais-data-chaos","2025-03-14",{"metadata":12981,"sys":12984,"fields":12994},{"tags":12982,"concepts":12983},[],[],{"space":12985,"id":12987,"type":14,"createdAt":12988,"updatedAt":12989,"environment":12990,"publishedVersion":846,"revision":939,"contentType":12992,"locale":672},{"sys":12986},{"type":656,"linkType":657,"id":658},"1jHG7IYbuYlqdqbxqAzwvm","2025-05-14T19:15:36.240Z","2026-07-01T13:04:36.400Z",{"sys":12991},{"id":664,"type":656,"linkType":665},{"sys":12993},{"type":656,"linkType":670,"id":12131},{"entryTitle":12995,"image":12996},"When HPC’s Straight Lines Intersect with AI’s Data Storm",{"metadata":12997,"sys":13000,"fields":13008},{"tags":12998,"concepts":12999},[],[],{"space":13001,"id":13003,"type":933,"createdAt":13004,"updatedAt":13005,"environment":13006,"publishedVersion":711,"revision":939,"locale":672},{"sys":13002},{"type":656,"linkType":657,"id":658},"tTNl1HJD6mn8utToBnFHT","2025-05-14T19:15:36.064Z","2026-07-01T13:04:35.417Z",{"sys":13007},{"id":664,"type":656,"linkType":665},{"title":12995,"description":12995,"file":13009},{"url":13010,"details":13011,"fileName":13014,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002FtTNl1HJD6mn8utToBnFHT\u002F0ebc456699c2c2ca1a93cc4e905a7f99\u002Fhpc.jpg",{"size":13012,"image":13013},186471,{"width":1143,"height":1144},"hpc.jpg",{"data":13016,"content":13017,"nodeType":1921},{},[13018,13025,13032,13039,13046,13053,13060,13067,13074,13081,13088,13095,13102,13109,13116,13123,13130,13137,13144,13151,13158,13165,13173,13199,13206,13213,13220,13227,13234,13241,13248,13255,13262,13269,13276,13283,13290,13297,13304,13311,13318,13325,13332,13339,13346,13353,13360],{"data":13019,"content":13020,"nodeType":1920},{},[13021],{"data":13022,"marks":13023,"value":13024,"nodeType":1906},{},[],"For decades, HPC sites ran on a single, unshakeable assumption: data moves in straight lines.",{"data":13026,"content":13027,"nodeType":1920},{},[13028],{"data":13029,"marks":13030,"value":13031,"nodeType":1906},{},[],"Massive files flowed through massive pipes into massive compute nodes, and parallel file systems were the arteries keeping the whole system pumping.",{"data":13033,"content":13034,"nodeType":1920},{},[13035],{"data":13036,"marks":13037,"value":13038,"nodeType":1906},{},[],"Sequential IO was the gold standard, and everything was engineered around it. Big blocks, big transfers, big throughput.",{"data":13040,"content":13041,"nodeType":1920},{},[13042],{"data":13043,"marks":13044,"value":13045,"nodeType":1906},{},[],"But AI data doesn’t move in straight lines. It’s a relentless churn of randomness, a constant loop of ingestion, training, inference, retraining—each step generating more data, more chaos, more noise.",{"data":13047,"content":13048,"nodeType":1920},{},[13049],{"data":13050,"marks":13051,"value":13052,"nodeType":1906},{},[],"What VAST Data’s Jan Heichler described at a recent HPC Advisory Council meeting is less a pipeline than it is a feedback loop.",{"data":13054,"content":13055,"nodeType":1920},{},[13056],{"data":13057,"marks":13058,"value":13059,"nodeType":1906},{},[],"And HPC’s parallel file systems, built to move structured blocks of data from disk to node and back again? Heichler says they’re drowning in the flood.",{"data":13061,"content":13062,"nodeType":1920},{},[13063],{"data":13064,"marks":13065,"value":13066,"nodeType":1906},{},[],"As he explained to the crowd gathered at European supercomputing hub, CSCS,  the old world of HPC, data was static, predictable, and finite. You knew what was coming in, how long it would take to process, and when it would be done. Climate simulations, genome sequencing, crash testing–all of these followed the same pattern: ingest data, run the model, spit out the result.",{"data":13068,"content":13069,"nodeType":1920},{},[13070],{"data":13071,"marks":13072,"value":13073,"nodeType":1906},{},[],"But AI factories don’t follow that pattern. They are relentless. Data doesn’t just flow in and out. It ricochets around the system, spawning new datasets, new training sets, new inferences, new transactions–each iteration generating more data, which in turn feeds back into the pipeline.",{"data":13075,"content":13076,"nodeType":1920},{},[13077],{"data":13078,"marks":13079,"value":13080,"nodeType":1906},{},[],"The data lifecycle in AI is a hurricane. And parallel file systems? They were built for a gentle breeze.",{"data":13082,"content":13083,"nodeType":1920},{},[13084],{"data":13085,"marks":13086,"value":13087,"nodeType":1906},{},[],"As Heichler explained, HPC’s reliance on large-block, sequential IO made sense when data was neatly staged and stored in predictable chunks. But AI is random by design. Datasets are fragmented, data flows are unpredictable, and every data point is a potential input for the next training run.",{"data":13089,"content":13090,"nodeType":1920},{},[13091],{"data":13092,"marks":13093,"value":13094,"nodeType":1906},{},[],"The very concept of “sequential IO” becomes meaningless when every data access is a scattershot read across hundreds of tiny files, each demanding immediate access.",{"data":13096,"content":13097,"nodeType":1920},{},[13098],{"data":13099,"marks":13100,"value":13101,"nodeType":1906},{},[],"And the problem isn’t just that data is random. It’s that AI is greedy.",{"data":13103,"content":13104,"nodeType":1920},{},[13105],{"data":13106,"marks":13107,"value":13108,"nodeType":1906},{},[],"In HPC, data sat in storage until it was needed. In AI, data is always in motion, always being processed, always being retrained. The same dataset that just finished training one model is immediately fed back into another. Every result generates more data, which in turn creates more training, which in turn creates more results.",{"data":13110,"content":13111,"nodeType":1920},{},[13112],{"data":13113,"marks":13114,"value":13115,"nodeType":1906},{},[],"The pipeline isn’t just a pipeline—it’s a cycle. A factory. A machine that never shuts down.",{"data":13117,"content":13118,"nodeType":1920},{},[13119],{"data":13120,"marks":13121,"value":13122,"nodeType":1906},{},[],"HPC systems were never built to handle that kind of load. Parallel file systems were engineered to deliver massive, sequential data transfers as fast as possible.",{"data":13124,"content":13125,"nodeType":1920},{},[13126],{"data":13127,"marks":13128,"value":13129,"nodeType":1906},{},[],"But AI data doesn’t arrive in massive blocks. It arrives in fragments, in wee-bitty, random IO bursts that hit the storage layer in unpredictable waves, slamming the system with a barrage of async reads and writes. And if the data isn’t accessible instantly, the entire pipeline stalls.",{"data":13131,"content":13132,"nodeType":1920},{},[13133],{"data":13134,"marks":13135,"value":13136,"nodeType":1906},{},[],"In the AI world, latency isn’t a nuisance, it’s a killer. If data can’t be accessed immediately, training grinds to a halt, inference stalls, and the entire AI factory seizes up.",{"data":13138,"content":13139,"nodeType":1920},{},[13140],{"data":13141,"marks":13142,"value":13143,"nodeType":1906},{},[],"Parallel file systems weren’t built for that kind of demand. They were built to handle big, predictable transfers, not the relentless, fragmented IOs that define AI pipelines.",{"data":13145,"content":13146,"nodeType":1920},{},[13147],{"data":13148,"marks":13149,"value":13150,"nodeType":1906},{},[],"So the AI builders moved on. They ditched tiering, shoved everything onto flash, and started treating every dataset—files, objects, vectors—as a single, unified pool of data that had to be accessible all the time. No staging. No waiting. Just immediate access across every layer of the pipeline. And every layer is generating more data, more IOs, more pressure on the system.",{"data":13152,"content":13153,"nodeType":1920},{},[13154],{"data":13155,"marks":13156,"value":13157,"nodeType":1906},{},[],"Meanwhile, as Heichler told the crowd of supercomputing practitioners, HPC keeps talking about optimizing sequential IO, as if AI workloads will someday magically conform to a single, predictable data flow. They won’t. AI pipelines are inherently chaotic.",{"data":13159,"content":13160,"nodeType":1920},{},[13161],{"data":13162,"marks":13163,"value":13164,"nodeType":1906},{},[],"As Heichler said, data doesn’t just move in one direction—it’s constantly folding back on itself, reprocessing its own outputs, generating new datasets on the fly.",{"data":13166,"content":13167,"nodeType":1920},{},[13168],{"data":13169,"marks":13170,"value":13172,"nodeType":1906},{},[13171],{"type":12164},"The AI Pipeline Isn’t a Flow—It’s a Factory That Never Shuts Down",{"data":13174,"content":13197,"nodeType":13198},{"target":13175},{"metadata":13176,"sys":13179,"fields":13187},{"tags":13177,"concepts":13178},[],[],{"space":13180,"id":13182,"type":933,"createdAt":13183,"updatedAt":13184,"environment":13185,"publishedVersion":810,"revision":939,"locale":672},{"sys":13181},{"type":656,"linkType":657,"id":658},"38i2MUkFE4UJUmd0cTS9Vh","2025-05-14T19:15:36.069Z","2026-07-01T13:04:35.419Z",{"sys":13186},{"id":664,"type":656,"linkType":665},{"title":13188,"description":13188,"file":13189},"AI pipeline Heichler",{"url":13190,"details":13191,"fileName":13196,"contentType":4468},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F38i2MUkFE4UJUmd0cTS9Vh\u002Fa08d327e87ceb686e9b5e115f2a0306d\u002Fherich1.png",{"size":13192,"image":13193},799220,{"width":13194,"height":13195},1612,917,"herich1.png",[],"embedded-asset-block",{"data":13200,"content":13201,"nodeType":1920},{},[13202],{"data":13203,"marks":13204,"value":13205,"nodeType":1906},{},[],"Here is the pipeline for AI as Heichler described it for the audience.",{"data":13207,"content":13208,"nodeType":1920},{},[13209],{"data":13210,"marks":13211,"value":13212,"nodeType":1906},{},[],"It all starts with petabytes data—raw, unfiltered, unrefined. Unstructured chaos streaming in from every conceivable source: transaction logs, sensor feeds, clickstreams, event data.",{"data":13214,"content":13215,"nodeType":1920},{},[13216],{"data":13217,"marks":13218,"value":13219,"nodeType":1906},{},[],"It’s a tidal wave of information, all of it waiting to be extracted, filtered, categorized, and compressed.",{"data":13221,"content":13222,"nodeType":1920},{},[13223],{"data":13224,"marks":13225,"value":13226,"nodeType":1906},{},[],"This is the data capture phase, the bottomless pit where the data pipeline begins. And in the AI world, it’s not just a one-time process. Every transaction, every query, every inference generates more data, which gets fed right back into the pipeline.",{"data":13228,"content":13229,"nodeType":1920},{},[13230],{"data":13231,"marks":13232,"value":13233,"nodeType":1906},{},[],"But raw data is useless without refinement, so enter data prep–a phase where CPUs grind through all that unstructured noise, transforming it into something coherent, something that can actually be fed into a model.",{"data":13235,"content":13236,"nodeType":1920},{},[13237],{"data":13238,"marks":13239,"value":13240,"nodeType":1906},{},[],"Extract, transform, deduplicate. Strip out the junk, compress the essentials. This is where AI starts to diverge sharply from HPC.",{"data":13242,"content":13243,"nodeType":1920},{},[13244],{"data":13245,"marks":13246,"value":13247,"nodeType":1906},{},[],"In HPC, data arrives in neat, massive blocks, ready to be processed. In AI, data doesn’t arrive. It accumulates, constantly, in shards and fragments, each a potential training set or inference point.",{"data":13249,"content":13250,"nodeType":1920},{},[13251],{"data":13252,"marks":13253,"value":13254,"nodeType":1906},{},[],"Data prep is the refinery where that noise gets distilled down to something GPUs can handle. And it doesn’t happen once. It’s perpetual.",{"data":13256,"content":13257,"nodeType":1920},{},[13258],{"data":13259,"marks":13260,"value":13261,"nodeType":1906},{},[],"Now, the refined data moves to model training, the place HPC people still think of as the heart of AI. And sure, it’s critical. This is where GPUs devour terabytes of structured datasets, smashing through training cycles, running model after model to extract patterns, insights, predictions.",{"data":13263,"content":13264,"nodeType":1920},{},[13265],{"data":13266,"marks":13267,"value":13268,"nodeType":1906},{},[],"But here’s the thing: in AI, the model is never finished. There’s no end point. Every model training run generates new data, new results, new feedback, which get fed right back into the pipeline. It’s a self-sustaining loop where data isn’t just consumed—it’s produced. And that production doesn’t stop once the model is trained.",{"data":13270,"content":13271,"nodeType":1920},{},[13272],{"data":13273,"marks":13274,"value":13275,"nodeType":1906},{},[],"Because now we’re moving to quantization. The bloated, overfed and power-hungry trained model needs to be slimmed down for inference.",{"data":13277,"content":13278,"nodeType":1920},{},[13279],{"data":13280,"marks":13281,"value":13282,"nodeType":1906},{},[],"The data pipeline doesn’t just compress raw data; it compresses models themselves, turning multi-terabyte monstrosities into quantized, inference-ready versions. These are still powerful, still packed with insight, but now they’re portable, scalable, ready to be deployed across CPUs, GPUs, whatever hardware is on hand.",{"data":13284,"content":13285,"nodeType":1920},{},[13286],{"data":13287,"marks":13288,"value":13289,"nodeType":1906},{},[],"But the pipeline isn’t done yet. The quantized model enters model serving, where the actual AI work happens. Every query, every inference request, every transaction spins off more data. A single request might generate a few kbs of inference output, but multiply that by millions of queries per second and suddenly you’re staring at another petabyte-scale data mountain.",{"data":13291,"content":13292,"nodeType":1920},{},[13293],{"data":13294,"marks":13295,"value":13296,"nodeType":1906},{},[],"And that data? It doesn’t just vanish. It gets logged, audited, traced, and fed right back into the pipeline.",{"data":13298,"content":13299,"nodeType":1920},{},[13300],{"data":13301,"marks":13302,"value":13303,"nodeType":1906},{},[],"Every response is a potential training set, every result a new dataset to be refined, quantized, and deployed again. The pipeline doesn’t flow—it loops and it’s here again where the division between HPC and AI sharpens further. ",{"data":13305,"content":13306,"nodeType":1920},{},[13307],{"data":13308,"marks":13309,"value":13310,"nodeType":1906},{},[],"And now to the transaction log store. Every data point, every response, every inference gets logged here, a relentless stream, all of it potential training data.",{"data":13312,"content":13313,"nodeType":1920},{},[13314],{"data":13315,"marks":13316,"value":13317,"nodeType":1906},{},[],"And through it all, a Zero Trust security layer weaves itself around every step. Because in AI, the data isn’t just flowing through a single pipeline. It’s looping through multiple systems, crossing hardware boundaries, hitting object stores, databases, training clusters, and inference engines.",{"data":13319,"content":13320,"nodeType":1920},{},[13321],{"data":13322,"marks":13323,"value":13324,"nodeType":1906},{},[],"As one might imagine, much of this does not look familiar to HPC shops.",{"data":13326,"content":13327,"nodeType":1920},{},[13328],{"data":13329,"marks":13330,"value":13331,"nodeType":1906},{},[],"In the kind of world shown above, the old HPC storage model isn’t just outdated or “AI ready”, it’s actually a bottleneck.",{"data":13333,"content":13334,"nodeType":1920},{},[13335],{"data":13336,"marks":13337,"value":13338,"nodeType":1906},{},[],"A system built to handle big blocks of data moving predictably through a pipe can’t handle hundreds of thousands of tiny IOs hitting the storage layer all at once, demanding immediate access. It’s like trying to funnel a waterfall through a garden hose.",{"data":13340,"content":13341,"nodeType":1920},{},[13342],{"data":13343,"marks":13344,"value":13345,"nodeType":1906},{},[],"So what happens to HPC’s parallel file systems? They’re still there and will be for a long time to come, pushing massive, sequential blocks of data through pipes that AI builders aren’t using anymore.",{"data":13347,"content":13348,"nodeType":1920},{},[13349],{"data":13350,"marks":13351,"value":13352,"nodeType":1906},{},[],"But in the AI world, parallel file systems have become a corner, an outdated, static backwater where big files go to sit and wait for compute nodes that no one is using.",{"data":13354,"content":13355,"nodeType":1920},{},[13356],{"data":13357,"marks":13358,"value":13359,"nodeType":1906},{},[],"And the AI builders? 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AI’s data lifecycle is a relentless, randomized churn, rendering the old storage model a bottleneck.",{"metadata":13511,"sys":13514,"fields":13522},{"tags":13512,"concepts":13513},[],[],{"space":13515,"id":13517,"type":933,"createdAt":13518,"updatedAt":13519,"environment":13520,"publishedVersion":711,"revision":939,"locale":672},{"sys":13516},{"type":656,"linkType":657,"id":658},"7zYesJgS0PnDh2cVYqwZtQ","2025-05-14T19:15:36.074Z","2026-07-01T13:04:35.421Z",{"sys":13521},{"id":664,"type":656,"linkType":665},{"title":13508,"description":13508,"file":13523},{"url":13524,"details":13525,"fileName":13528,"contentType":1459},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F7zYesJgS0PnDh2cVYqwZtQ\u002Fe53d1b31e1e22d51a4039cbb76c739f8\u002Fheichsoc.jpg",{"size":13526,"image":13527},432708,{"width":1456,"height":1457},"heichsoc.jpg",{"metadata":13530,"sys":13547,"fields":13557},{"tags":13531,"concepts":13546},[13532,13535,13538,13540,13543],{"sys":13533},{"type":656,"linkType":1501,"id":13534},"ai",{"sys":13536},{"type":656,"linkType":1501,"id":13537},"funnelAwareness",{"sys":13539},{"type":656,"linkType":1501,"id":4363},{"sys":13541},{"type":656,"linkType":1501,"id":13542},"perspectives",{"sys":13544},{"type":656,"linkType":1501,"id":13545},"useCaseHpc",[],{"space":13548,"id":13550,"type":14,"createdAt":13551,"updatedAt":13552,"environment":13553,"publishedVersion":1817,"revision":691,"contentType":13555,"locale":672},{"sys":13549},{"type":656,"linkType":657,"id":658},"3oK3UwdDw076oDjB2H3tbx","2025-03-03T12:50:01.879Z","2026-07-01T13:04:36.450Z",{"sys":13554},{"id":664,"type":656,"linkType":665},{"sys":13556},{"type":656,"linkType":670,"id":12975},{"title":13558,"slug":13559,"publishedDate":13560,"author":13561,"thumbnailImage":13562,"middleContent":13596,"socialIcons":13702,"hidden":19,"seo":13828},"5 Ways to Rethink AI Storage and Avoid HPC-Era Pitfalls","5-ways-to-rethink-ai-storage-and-avoid-hpc-era-pitfalls","2025-03-03","Brian Verkley, Director of AI",{"metadata":13563,"sys":13566,"fields":13576},{"tags":13564,"concepts":13565},[],[],{"space":13567,"id":13569,"type":14,"createdAt":13570,"updatedAt":13571,"environment":13572,"publishedVersion":846,"revision":939,"contentType":13574,"locale":672},{"sys":13568},{"type":656,"linkType":657,"id":658},"4xx2KGGMBKLrvTR4RypY5h","2025-02-27T21:33:36.299Z","2026-07-01T13:04:36.471Z",{"sys":13573},{"id":664,"type":656,"linkType":665},{"sys":13575},{"type":656,"linkType":670,"id":12131},{"entryTitle":13558,"image":13577},{"metadata":13578,"sys":13581,"fields":13589},{"tags":13579,"concepts":13580},[],[],{"space":13582,"id":13584,"type":933,"createdAt":13585,"updatedAt":13586,"environment":13587,"publishedVersion":711,"revision":939,"locale":672},{"sys":13583},{"type":656,"linkType":657,"id":658},"1UBYnQiKhNlYn2WfbX7O71","2025-02-27T21:33:32.419Z","2026-07-01T13:04:35.422Z",{"sys":13588},{"id":664,"type":656,"linkType":665},{"title":13558,"description":13558,"file":13590},{"url":13591,"details":13592,"fileName":13595,"contentType":1146},"\u002F\u002Fimages.ctfassets.net\u002F2f3meiv6rg5s\u002F1UBYnQiKhNlYn2WfbX7O71\u002F5fc3e314138effed03c4509e23a57e72\u002F5-ways-to-rethink-ai-storage-and-avoid-hpc-era-pitfalls.webp",{"size":13593,"image":13594},129718,{"width":1143,"height":1144},"5-ways-to-rethink-ai-storage-and-avoid-hpc-era-pitfalls.webp",{"data":13597,"content":13598,"nodeType":1921},{},[13599,13606,13614,13621,13628,13635,13642,13649,13656,13663,13670,13677,13684],{"data":13600,"content":13601,"nodeType":1920},{},[13602],{"data":13603,"marks":13604,"value":13605,"nodeType":1906},{},[],"Techniques that once drove HPC performance—like extreme bandwidth at any cost—can now work against AI projects that need to scale efficiently. Below is a structured look at key steps that help leaders in AI infrastructure design balance power considerations, capacity, and the performance needs of modern AI clusters.",{"data":13607,"content":13608,"nodeType":13613},{},[13609],{"data":13610,"marks":13611,"value":13612,"nodeType":1906},{},[],"1. Power is Scarce, Performance is Not","heading-4",{"data":13615,"content":13616,"nodeType":1920},{},[13617],{"data":13618,"marks":13619,"value":13620,"nodeType":1906},{},[],"Traditional HPC systems focused on raw bandwidth, but today’s AI clusters face a new bottleneck: power. Training clusters with thousands of GPUs can quickly reach power draws in the megawatt range. Every watt that goes to spinning disks or smaller SSDs is a watt you cannot allocate to compute. One reason is that data centers often have tight power caps, not to mention timelines for expanding grid capacity that extend over years. By accepting that power is finite, you start to look for storage solutions that minimize energy overhead while still delivering the necessary I\u002FO speed for AI.",{"data":13622,"content":13623,"nodeType":13613},{},[13624],{"data":13625,"marks":13626,"value":13627,"nodeType":1906},{},[],"2. Calculate the True Cost of Storage Overkill",{"data":13629,"content":13630,"nodeType":1920},{},[13631],{"data":13632,"marks":13633,"value":13634,"nodeType":1906},{},[],"HPC configurations often revolve around maximizing performance per terabyte, which may involve deploying vast numbers of smaller SSDs. This approach might look appealing on a specification sheet, but in a multi-petabyte or exabyte environment, each drive draws additional power around the clock. For instance, a difference of 100,000 extra SSDs for a two-exabyte system can result in a multi-megawatt hike in power usage. That is enough energy to fuel over a thousand GPUs instead. When you weigh these tradeoffs, the cost of HPC-style overkill becomes impossible to ignore.",{"data":13636,"content":13637,"nodeType":13613},{},[13638],{"data":13639,"marks":13640,"value":13641,"nodeType":1906},{},[],"3. Remember That Capacity Delivers Parallel Performance",{"data":13643,"content":13644,"nodeType":1920},{},[13645],{"data":13646,"marks":13647,"value":13648,"nodeType":1906},{},[],"Large-format SSDs, in the 60TB–120TB range, bring a surprising amount of performance to AI workloads. If you distribute data across thousands of these higher-capacity drives, your overall throughput can be enormous. This parallelism means that even demanding tasks like checkpointing multi-terabyte models (common in large language model training) can finish on schedule. The real benefit is that you achieve strong I\u002FO performance while minimizing the number of drives—and thereby your power draw.",{"data":13650,"content":13651,"nodeType":13613},{},[13652],{"data":13653,"marks":13654,"value":13655,"nodeType":1906},{},[],"4. Be Wary of Tiered Storage and Manual Movement",{"data":13657,"content":13658,"nodeType":1920},{},[13659],{"data":13660,"marks":13661,"value":13662,"nodeType":1906},{},[],"HPC best practices might suggest a separate “scratch” layer to capture temporary high-velocity data. That can be helpful when your workloads revolve around large, carefully scheduled batch jobs. AI training, however, is more dynamic and often demands rapid iteration. Adding another layer of data management can introduce delays and complexity. Single-tier, all-flash systems can eliminate the burden of moving data back and forth between tiers. This simpler design also consumes fewer resources overall, since you don’t need to power entire arrays dedicated to short-term storage.",{"data":13664,"content":13665,"nodeType":13613},{},[13666],{"data":13667,"marks":13668,"value":13669,"nodeType":1906},{},[],"5. Optimize Checkpointing with Asynchronous Writes",{"data":13671,"content":13672,"nodeType":1920},{},[13673],{"data":13674,"marks":13675,"value":13676,"nodeType":1906},{},[],"Large AI models need consistent checkpointing to safeguard progress, which can create bursts of high I\u002FO. A well-designed storage system with many parallel SSDs can handle these writes without slowing the GPUs. More recently, the industry has shifted increasingly to checkpointing asynchronously, whereby the compute cluster continues its training work while local copies of checkpoint data are written to remote storage in the background. As a result, most modern AI infrastructure no longer needs a separate high-performance scratch storage system, resulting in a smaller hardware footprint, thus saving both power and space.",{"data":13678,"content":13679,"nodeType":1920},{},[13680],{"data":13681,"marks":13682,"value":13683,"nodeType":1906},{},[],"These steps illustrate why HPC-era approaches can hamper AI progress at very large scales. By accepting that power is finite and evaluating storage choices in light of that, you gain flexibility for where it matters most—training. Focusing on fewer, higher-capacity drives and streamlined design helps AI initiatives avoid bottlenecks and reach model objectives more efficiently. The opportunity is significant, and it begins with reimagining how storage fits into a next-generation AI data center.",{"data":13685,"content":13686,"nodeType":1920},{},[13687,13691,13698],{"data":13688,"marks":13689,"value":13690,"nodeType":1906},{},[],"What are we missing? What are your experiences designing storage for HPC and AI environments? Join the conversation on ",{"data":13692,"content":13693,"nodeType":1915},{"uri":8966},[13694],{"data":13695,"marks":13696,"value":13697,"nodeType":1906},{},[],"Cosmos",{"data":13699,"marks":13700,"value":13701,"nodeType":1906},{},[],", the VAST community for AI professionals and innovators to share insights and build lasting partnerships across the global 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a recent ",{"data":13942,"content":13944,"nodeType":1915},{"uri":13943},"..\u002F..\u002Fresources\u002Fforms\u002Fwebinars\u002F5-reasons-why-pfs-are-not-a-silver-bullet-for-ai",[13945],{"data":13946,"marks":13947,"value":13949,"nodeType":1906},{},[13948],{"type":12115},"webinar",{"data":13951,"marks":13952,"value":13954,"nodeType":1906},{},[13953],{"type":12115}," VAST VP of Systems Engineering Subramanian Kartik and I tackled the implications of a shift in AI infrastructure from legacy parallel file systems to modern all-flash platforms like VAST. This blog summarizes the key points we discussed.",{"data":13956,"content":13957,"nodeType":1920},{},[13958,13961,13969],{"data":13959,"marks":13960,"value":2030,"nodeType":1906},{},[],{"data":13962,"content":13964,"nodeType":1915},{"uri":13963},"..\u002F..\u002Fglossary\u002Fhpc\u002F",[13965],{"data":13966,"marks":13967,"value":13968,"nodeType":1906},{},[],"HPC",{"data":13970,"marks":13971,"value":13972,"nodeType":1906},{},[]," High-performance computing (HPC) harnesses the power of computer clusters to solve complex problems with massive data sets. Workloads have traditionally run on parallel file systems due to their large block and sequential IO patterns. AI workloads, however, require a different type of system and are in contrast mainly based on read IO patterns, and especially by random read. ",{"data":13974,"content":13975,"nodeType":1920},{},[13976],{"data":13977,"marks":13978,"value":13979,"nodeType":1906},{},[],"To accommodate this evolution, the industry is transitioning to all-flash based systems powered by SSDs. All-flash systems are better equipped to handle both HPC and AI workloads. In addition, SSDs provide easier accessibility to the entire namespace, which would be hard to achieve using HDDs. ",{"data":13981,"content":13982,"nodeType":1920},{},[13983],{"data":13984,"marks":13985,"value":13986,"nodeType":1906},{},[],"The following are insights from the discussion around questions that have arisen about this recent change in workloads, storage, and infrastructure.",{"data":13988,"content":13989,"nodeType":13995},{},[13990],{"data":13991,"marks":13992,"value":13994,"nodeType":1906},{},[13993],{"type":12164},"For Parallel File Systems, High Performance Equals High Complexity","heading-3",{"data":13997,"content":13998,"nodeType":1920},{},[13999],{"data":14000,"marks":14001,"value":14002,"nodeType":1906},{},[],"While parallel file systems have been the go-to for HPC workloads, they come with challenges. Parallel file systems are difficult to maintain and require additional client side considerations. Upgrade tasks become complicated due to their complexity.",{"data":14004,"content":14005,"nodeType":1920},{},[14006],{"data":14007,"marks":14008,"value":14009,"nodeType":1906},{},[],"VAST Data’s technology offers high performance without the complexity or special network infrastructure. Throughput of VAST clusters can deliver terabytes per second in read throughput for thousands of clients, plus individual amounts of 170 GB\u002Fsecond for single mount points from a single client.",{"data":14011,"content":14012,"nodeType":1920},{},[14013],{"data":14014,"marks":14015,"value":14016,"nodeType":1906},{},[],"NFS and S3 protocols have comparable performance levels with VAST and S3 is becoming more popular due to its use in Hadoop, deep learning, Apache Spark, Cassandra, and Spark ecosystems.",{"data":14018,"content":14019,"nodeType":13995},{},[14020],{"data":14021,"marks":14022,"value":14024,"nodeType":1906},{},[14023],{"type":12164},"Don’t AI workloads require a lot of write performance that VAST can’t deliver?",{"data":14026,"content":14027,"nodeType":1920},{},[14028],{"data":14029,"marks":14030,"value":14031,"nodeType":1906},{},[],"For those wondering if VAST can deliver sufficient write performance for AI, it’s important to note that 95 percent of AI workloads are read-intensive when dealing with customers. This runs contrary to the perceived need for an even balance between reads and writes. Exceptions exist (e.g., HPC or Large Language Model checkpointing) but AI workloads are largely dominated by reads. The right amount of read\u002Fwrite bandwidth must be met to successfully complete tasks.",{"data":14033,"content":14034,"nodeType":1920},{},[14035],{"data":14036,"marks":14037,"value":14038,"nodeType":1906},{},[],"Additionally, flash technology is becoming increasingly affordable and surpassing the capabilities of hard drives, while having a lower environmental impact and higher memory density.",{"data":14040,"content":14041,"nodeType":13995},{},[14042],{"data":14043,"marks":14044,"value":14046,"nodeType":1906},{},[14045],{"type":12164},"Parallel File Systems are Not Built for Non-Disruptive Ops",{"data":14048,"content":14049,"nodeType":1920},{},[14050],{"data":14051,"marks":14052,"value":14053,"nodeType":1906},{},[],"File systems running in parallel or in clusters are fragile in terms of non-disruptive operations. But VAST boasts of an architecture that guarantees 100% uptime, along with freedom from maintenance operations interruptions. How? Simple! VAST’s single class storage works for clusters of all sizes without the need for any manual data layout or tweaking knobs. VAST also exposes its namespace through industry-standard protocols, not requiring native proprietary clients.",{"data":14055,"content":14056,"nodeType":1920},{},[14057,14061,14069,14073,14081,14085,14089],{"data":14058,"marks":14059,"value":14060,"nodeType":1906},{},[],"Isilon\u002FPowerScale customers have already seen the benefits of the product, as VAST saves session state for stateful protocols in persistent ",{"data":14062,"content":14064,"nodeType":1915},{"uri":14063},"..\u002F..\u002Fglossary\u002Fnvme\u002F",[14065],{"data":14066,"marks":14067,"value":14068,"nodeType":1906},{},[],"NVMe",{"data":14070,"marks":14071,"value":14072,"nodeType":1906},{},[],". NVMe is a new protocol designed specifically to provide systems access to non-volatile memory devices like SSDs. NVMe has much lower overhead, and allows much more parallel I\u002FO than the older SCSI protocol, thus ensuring even ",{"data":14074,"content":14076,"nodeType":1915},{"uri":14075},"..\u002F..\u002Fglossary\u002Fsmb\u002F",[14077],{"data":14078,"marks":14079,"value":14080,"nodeType":1906},{},[],"SMB",{"data":14082,"marks":14083,"value":14084,"nodeType":1906},{},[]," ",{"data":14086,"marks":14087,"value":14088,"nodeType":1906},{},[],"(Server Message Block)",{"data":14090,"marks":14091,"value":14092,"nodeType":1906},{},[]," is the file protocol developed by Microsoft. The now obsolete and deprecated version 1.0 was known as CIFS. Primary file protocol for Windows and OS X. 2 sessions will not suffer a disconnect in a rolling upgrade. Sounds impressive, right? ",{"data":14094,"content":14095,"nodeType":1920},{},[14096],{"data":14097,"marks":14098,"value":14099,"nodeType":1906},{},[],"VAST’s Docker containers eliminate the complexity of technology and allow for fast upgrades and restarts, without worrying about meta-data servers or carving stuff out. Additionally, setting up file shares or exporting data is effortless. ",{"data":14101,"content":14102,"nodeType":1920},{},[14103],{"data":14104,"marks":14105,"value":14106,"nodeType":1906},{},[],"Once introduced to your stack, VAST could assure near-to-zero trouble tickets and seamless functioning of your system with minimal effort.",{"data":14108,"content":14109,"nodeType":13995},{},[14110],{"data":14111,"marks":14112,"value":14114,"nodeType":1906},{},[14113],{"type":12164},"Proprietary File System Clients are a Necessary Evil",{"data":14116,"content":14117,"nodeType":1920},{},[14118],{"data":14119,"marks":14120,"value":14121,"nodeType":1906},{},[],"Proprietary file system clients can be seen as a necessary evil to achieve optimal performance. However, these native clients come with their own set of limitations, like being compatible with only one storage platform and needing upgrades when the underlying system is changed.",{"data":14123,"content":14124,"nodeType":1920},{},[14125],{"data":14126,"marks":14127,"value":14128,"nodeType":1906},{},[],"Luckily, advanced methods have been developed that use industry-standard clients. Users can now switch platforms seamlessly without making any changes on the client side. VAST engineers have practical experience with this particular issue and have seen some unique scenarios where the client can become part of the file system and interact in peculiar ways.",{"data":14130,"content":14131,"nodeType":1920},{},[14132],{"data":14133,"marks":14134,"value":14135,"nodeType":1906},{},[],"For instance, Alphafold, a DeepMind program that solves the complex mathematical problem of protein-folding, makes heavy use of mmap() files. Upon running on VAST  system, Alphafold sees  an improvement of 500-700 percent, compared to a parallel file system. VAST has significant experience with this, so let us help you navigate this complex world of file systems.",{"data":14137,"content":14138,"nodeType":13995},{},[14139],{"data":14140,"marks":14141,"value":14143,"nodeType":1906},{},[14142],{"type":12164},"Science Project Deployments",{"data":14145,"content":14146,"nodeType":1920},{},[14147],{"data":14148,"marks":14149,"value":14150,"nodeType":1906},{},[],"To develop and deploy heavy-use parallel file systems, an exceptional team with a deep understanding of code and operating systems is essential. This is particularly true with parallel file systems, which are more common in National Labs due to their access to top-tier expertise.",{"data":14152,"content":14153,"nodeType":1920},{},[14154],{"data":14155,"marks":14156,"value":14157,"nodeType":1906},{},[],"Unfortunately, many commercial enterprises lack these resources, creating a critical need for sustainable solutions that enable businesses to compete. Even higher education institutions benefit from using parallel file systems, thanks to the availability of graduate students and postdocs who can assist with operations.",{"data":14159,"content":14160,"nodeType":1920},{},[14161],{"data":14162,"marks":14163,"value":14164,"nodeType":1906},{},[],"But deploying these solutions requires far more than technical know-how. 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