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TOP500 at ISC’26: We have a New Number 1 Supercomputer
- techsystems 4mo agoIs it the first to reach 2 exaflops?
- adrian_b 4mo agoYes. The fastest US supercomputer, El Capitan at Lawrence Livermore National Laboratory, reaches only 1.809 exaflops, while this reaches 2.198, 22% higher. On the HPCG benchmark, which is more strongly influenced by memory bandwidth, the advance over the GPU-based El Capitan is even greater, of 26.4%.
- 2OEH8eoCRo0 4mo agoExtremely impressive accomplishment considering they did this with Chinese interconnects and Chinese chips. This is a wake up call.
- echelon 4mo agoWe're too busy regulating the tech, not granting access to US engineers and companies, arguing against power and data centers, stopping skilled immigration. This is absolutely going to bite us in the face in five to ten years.
- 2OEH8eoCRo0 4mo agoSeparate issue that has nothing to do with US manufacturing or HPC. I think our retreat from science funding and offshoring advanced manufacturing is a bigger issue.
- jandrewrogers 4mo agoTOP500 can be done with inexpensive silicon. It is more about a willingness to aggregate enough hardware in one place. As a benchmark, it tells you almost nothing about computing power or scalability for other applications because it doesn't exercise the bottlenecks most high-scale applications have.
- NitpickLawyer 4mo ago> TOP500 can be done with inexpensive silicon. Didn't the DoD at one point build a 1k+ PS3 cluster based on their multi-core chip and had a mini supercomputer CotS? I remember Sony not liking that people were buying them for other things rather than gaming (iirc they were losing money on hardware at the time) so they bricked linux support soon after.
- packetslave 4mo ago> Didn't the DoD at one point build a 1k+ PS3 cluster based on their multi-core chip and had a mini supercomputer CotS? The Air Force did (Condor) and it hit #33 on the 2010 Top500.
- rbanffy 4mo agoThe PS3 was sold at a loss, so their numbers are skewed.
- numpad0 4mo agoNot the first time that happened, even for China: https://en.wikipedia.org/wiki/List_of_fastest_computers https://en.wikipedia.org/wiki/List_of_fastest_computers
- adrian_b 4mo agoThis is quite different. The last time when China had the fastest supercomputer, it was more than 20 times slower than this one and more than 8 times less efficient in energy consumption. Moreover, its capability was overestimated by the Linpack benchmarks and in other workloads its performance was much less impressive. For this system, it is the opposite situation. Its result in Top500 underestimates it capability. In other more demanding workloads, where the influence of the memory bandwidth and latency is stronger, its advantage over the US supercomputers is greater than in Top500.
- numpad0 4mo agoIt's the same. Nobody was buying POWER9 or A64FX systems, and manufacturers of those are no factor to anything. One could theoretically drive home with a ready-to-go rack of non-American and/or non-x86 supercomputer nodes at any point in time across the last few decades, sometimes even with non-NVIDIA/AMD massively parallel coprocessor cards. Nobody did. If China(or any country) would _ship_ these alternative supercomputer hardware, only then anything could change.
- rippeltippel 4mo agoPreviously on HN: https://news.ycombinator.com/item?id=48658334 https://news.ycombinator.com/item?id=48658334
- speedgoose 4mo agoNo comments there though.
- lokimedes 4mo agoWould the AI “GW-scale” clusters be able to run the Top500 benchmarks meaningfully? And what might be the outcome?
- wmf 4mo agoYes, they should score well on Linpack as long as they use Ozaki emulation.
- adrian_b 4mo agoNo, that is too slow. Most claims about the cost of emulating FP64 on GPUs are wrong, because they assume that only the significand of floating-point numbers must be extended. In reality it is even more important to extend the exponent, because with the exponent of FP32 overflows would be much too frequent in scientific/technical computations to accomplish anything. The minimum FP64 emulation on FP32-capable GPUs requires 3 numbers per emulated FP64, which may be 3 FP32 numbers, or the exponent may be an Int32, if that works better on the target GPU. An emulated FP64 operation is likely to be at least 20 times slower than a FP32 operation. That is much faster than the 1:64 ratio provided in hardware by an NVIDIA GPU, but even on the fastest FP32 GPUs it is too slow to compete with CPUs, in a professional setting. FP64 emulation on a GPU can be useful only in a home computer, which may have a rather weak CPU and increasing the FP64 throughput using the GPU can be done at no additional cost, so it can be worthwhile.
- mrlongroots 4mo ago> And what might be the outcome? DoE compute budgets are ~10B USD across labs. AI training is a trillion-dollar workload. Different league.
- adrian_b 4mo agoNo. The AI oriented GPUs or TPUs have either weak FP64 throughput or they may not support FP64 at all. They can compete neither with CPUs nor with GPUs that have good FP64 support, like the AMD CDNA datacenter GPUs, which occupy all the top places among American supercomputers. NVIDIA has stopped improving the FP64 throughput even in their "datacenter" GPUs, abandoning this nowadays smaller market to AMD. The AMD CDNA GPUs can be used for both HPC and AI, so only an AI cluster based on them could have dual use, but most who want AI choose NVIDIA.
- brianolson 4mo ago> Why aren’t these AI companies submitting to the TOP500 to show off their computing prowess? my knowledge is 10+ years out of date, but once upon a time if they'd chosen to, Google could have had _several_ entries in the top 10 of the TOP500 list It's just poker, they didn't want to tip their hand
- iberator 4mo agoCloud computing is not a supercomputer. Different architecture, bandwitch, interconnectivity and latencies.
- dgacmu 4mo agoThat's not nearly as true when you look at AI training clusters. They're basically supercomputers but without an FP64 focus. (These are the systems to which GP was referring at Google.)
- jeffbee 4mo agoI thought TPUs couldn't reasonably run LINPACK at all because TPUs do not acknowledge that FP64 exists. I know Google wants to compare their stuff to El Capitan or whatever but the comparison does not seem valid to me.
- cynicalkane 4mo agoEven before AI training clusters became important, Google has had an outstanding custom fabric (there's papers about it) together with the ability to tune NICs for their own cases, and "their own cases" meant nearly everything engineered within Google. Ethernet hardware has had low kernel latency and DMA for a long time; it's the rest of the stack that hurts. But as far back as the early 2010s (if not further back, that goes beyond my knowledge horizon), you could just make it not hurt, if you had the software engineers to do it.
- mrlongroots 4mo agoAll that would not help you with an AI training cluster interconnect. See Amin Vahdat's keynote at HotInterconnects 2025. Everyone is building a fabric for this stuff from scratch (Google/Falcon, Amazon/EFA, Azure/MANA, Cornelis/CN5000, and obviously Mellanox).
- dgellow 4mo agoJust glad to see Hamburg mentioned :) Hope you all didn’t suffer too much through the current heatwave
- jandrewrogers 4mo agoTOP500 hasn't been a particularly useful measure of practical computing power in modern systems for many years because what it measures isn't a significant bottleneck in most real systems. It has become a measure of how much money someone is willing to spend for bragging rights. (HPCG is better in that it is a bit more bandwidth focused but still pretty narrow.) Most companies with huge systems don't participate.
- bee_rider 4mo agoI wonder if there would have been an opportunity to generate some finer-grained benchmarks with something like BiCGStab+ILU (or maybe CG+incomplete cholesky). Instead of CG+Gauss Seidel. The pitch being, you might have made different memory vs compute trade-offs with designing your cluster, but you should be able to select a fill-in factor for the preconditioner to suit it.
- jandrewrogers 4mo agoI think you could build more representative benchmarks that capture capability better. The tension is that HPC companies are pure hardware companies and you need a lot of help from software to make your hardware look good. They don’t like that. Most of our software sucks at the scales they try to benchmark. Ironically, the related Graph500 benchmarks reflect this better. Performance is dependent more on using the hardware better than better hardware per se.
- adrian_b 4mo agoThis seems like a "sour grapes" comment. The new Chinese supercomputer beats all US supercomputers also in HPCG, not only in Linpack. What is remarkable is that this was done despite the US attempts of sabotaging HPC in China by "sanctions". This uses custom CPUs designed in China, which implement an Armv9-A ISA with SME (scalable matrix extension) and which use fast HBM memory. These CPUs are fast enough that they do not need any GPUs for exceeding the throughput of the American supercomputers, which use GPUs. This is like in the Japanese Fugaku, which was the first to implement the Armv8-A ISA with SVE, but which now is rather old. Like in all CPU-based supercomputers, for this new Chinese supercomputer it is much easier to reach a higher percentage of the theoretical maximum throughput, when solving any problem. So for most practical problems it will be faster than a GPU-based supercomputer that would have the same theoretical maximum throughput. So this is a much more interesting supercomputer than those built by just buying some HPC racks from HPE (Cray). Because China was forbidden to buy the American equipment, they had to innovate and design their own. Eventually they made something better than what they could not buy.
- ziofill 4mo ago> Two cores are disabled per cluster. I’m sure there is a good reason for this, which is..?
- jandrewrogers 4mo agoIt is likely that those cores are dedicated to unrelated management, monitoring, and administrative tasks. This is common and many workloads are throttled on bandwidth anyway. For the purposes of the benchmark, those cores are not participating in the workload.
- brianolson 4mo agoYield. Some fraction of cores had a speck of dust or something, but at 38/40 good cores per chip they got economical yield
- dist-epoch 4mo agoCouldn't some chips have 40 good cores, while others have only 36? Do they all need to be exactly 38?
- tjhei 4mo agoAnd then even if some nodes had 40/40 "good" cores, it would make load balancing a lot more complicated if core counts vary. Easier to turn them off at the hardware level.
- flopsamjetsam 4mo ago> We think it is highly likely that these LX2 chiplets are etched using SMIC 7 nanometer processes at the N+3 refinement, and we base that on the fact that the chip only runs at 1.55 GHz. That is nowhere near the 3 GHz that SMIC can push with that process, but it is probably lower to get the memory and core speeds more balanced. [1] Based on the ARMv9.2. [1] https://www.nextplatform.com/hpc/2026/06/25/a-deep-dive-on-chinas-lineshine-all-cpu-exaflops-class-supercomputer/5262439 https://www.nextplatform.com/hpc/2026/06/25/a-deep-dive-on-c...
- adrian_b 4mo agoDespite what it says at that link, it is more likely to be based on Armv9.3-A ISA, because it supports SME. In the CPU cores designed by the Arm company, SME has been added only in the latest generation of Armv9.3-A CPUs, which was launched last year. For each level of Armv9, there are many mandatory features and many optional features. If the Chinese CPU does not implement all the mandatory Armv9.3-A features (and we do not know anything about this), then it will still be considered only an Armv9.2-A CPU, but even in that case it should be referred as an Armv9.2-A + SME, in order to not confuse it with the Armv9.2-A CPUs that have been used for a few years in smartphones, laptops and mini-PCs and which do not have SME, so they cannot have a comparable performance.
- amelius 4mo agoHow many tokens/s? :)
- Retr0id 4mo agoInteresting to see PAC mentioned on the slide, I'd have assumed security features would be a waste of transistors on something so compute-optimized - but maybe they want to isolate workloads from each other?
- fragmede 4mo agoYes exactly. it means you can sell isolated work units. With cloud being, well, cloud, if you can do that, you then have a market, with bidders able to bid higher for more capacity right now, vs a lower bid for eventually.
- b33f 4mo agoWhy are they not using GPUs? is it use cases that don't suit GPUs or because of the limitations they are imposing on themselves to use SMIC domestic chips?
- amelius 4mo agoGPUs are for graphics (the G in GPU). These systems are used for more general computations.
- galaxy_quest 4mo agoI’m not sure if I’m missing a joke, but that’s why we have general purpose computing on graphics processing units (GPGPU) which is why 8/10 of the top 10 machines have GPUs.
- antonvs 4mo agoGPUs were for graphics. Now, they're mainly used for machine learning training and inference. The big tech companies are spending eye-watering amounts for GPUs - hundreds of billions of dollars a year each. That's the reason that Nvidia's market cap is at $4.66 trillion.
- wmf 4mo agoI suspect Chinese GPUs (e.g. Biren) are not mature.
- adrian_b 4mo agoIf you can avoid GPUs, that is preferable. The reason is that with GPUs it is far more difficult to reach a great percentage of the maximum theoretical throughput. Most GPU programs reach only a very small fraction of what is theoretically possible, and in the best cases one may reach something like 50% to 60% of the maximum. This CPU-based supercomputer has demonstrated reaching 80% of the theoretical maximum throughput, and this is typical for CPU-based supercomputers. It is much easier to write efficient programs for CPUs. The new custom Chinese CPUs, which use SME, the Arm Scalable Matrix Extension, are fast enough that they have beaten all GPU-based supercomputers, so there was no need to use GPUs. Moreover these CPUs use HBM for a very fast memory interface, so in the benchmarks that depend more on memory bandwidth they have an even greater advance over the US GPU-based supercomputers. Thus there really was no point in using GPUs. GPUs are necessary only when your CPUs are not good enough, which was not the case here. In the recent past, the Japanese Fugaku used the same approach, of avoiding GPUs. At that time, their custom CPUs using the Armv8-A ISA with SVE were the first which used this ISA in HPC, but now that ISA variant is obsolete in comparison with the Armv9-A ISA with SME, which is implemented in these new custom Chinese CPUs.
- chrisss395 4mo agoI haven't kept up with the latest on supercomputing power, but I recall some years ago there being strong evidence that China had a couple of un-announced supercomputers that would have topped the charts. It makes me wonder what is publicly disclosed vs. actual.
- mlmonkey 4mo agoYou can see the list yourself at https://top500.org/ https://top500.org/ Deep link: https://top500.org/lists/top500/list/2026/06/ https://top500.org/lists/top500/list/2026/06/
- TacticalCoder 4mo agoAnd you know it needs a well-conceived OS to be able to run it: it's got to be... Microsoft Windows right? The OS powering 0% of the 500 supercomputers of the Top 500. But this time, it has to be Windows, right? Amirite? Ah, no, just kidding: it's "Kylin OS". It used to be a BSD derivative and now it's just based on Linux. I know, I know: "It's a heavily modified Linux". Whatever, it's not Windows and that makes me very happy.
- adrian_b 4mo agoAdditional details in: https://arxiv.org/abs/2605.08633v1 https://arxiv.org/abs/2605.08633v1 https://www.servethehome.com/arm-cpus-take-number-1-in-latest-top500-list-with-chinese-lineshine/ https://www.servethehome.com/arm-cpus-take-number-1-in-lates... https://www.top500.org/news/lineshine-debuts-no-1-top500-enters-new-global-exascale-era/ https://www.top500.org/news/lineshine-debuts-no-1-top500-ent...
- charcircuit 4mo agoShouldn't AWS be considered the Number 1 super computer? The amount of compute, storage, memory, etc available is ginormous.
- Alien1Being 4mo agoChina keeps winning....
- rbanffy 4mo agoChina is likely the dominant superpower in this century. They have the manufacture capability and the numbers for that.
- rbanffy 4mo agoI wonder if the CPUs can be obtained for, say, a deskside workstation.
- bfgpereira 4mo agoThe TOP500 rank is not a count of nodes, GPUs, or aggregate FLOPS. Many systems have the node count to be able to run such benchmarks, but are not optimized or even capable of running them. Having these systems run these large calculations in a sustained way, and performing, is what separates a bunch of nodes together in a data center from an actual cluster that is able to run a benchmark like HPL or HPCG. To sustain 70 - 80% of peak performance across hundreds of thousands of cores, you need a real low-diameter, high-bandwidth, low-latency fabric and a balanced memory subsystem, running on a system w almost no failures or network issues. A loosely-coupled cluster with an oversubscribed fat-tree will 'run Linpack' and then post an Rmax that's a small fraction of its naive peak. Also, have a look at the Green500, and the systems there. This is not about bragging rights vendors, this is about placing commodity hardware, tuning it, and bringing it up to health in a way that squeezes all of that last performance possible out of the clusters on those lists. That's the opposite of vendor flexing - it's a craft that you cannot see in a simple node count, as some have been comparing here. If you ever worked on this field, and with the vendors at this scale, you would know. Its not easy, its actually very hard. ... and imagine you need to deploy this, systems at this scale, w technologies that are sometimes just emerging and sometimes even need proper field testing * every 6 months * to be able to reach the scale and stability to land on these lists.