IMO the coolest thing about Summit/Sierra is that the GPUs and CPUs have a fully coherent single address space with all memory available to the GPUs by default, meaning that your stack- and malloc-allocated variables can be used directly from the GPUs.
However, maybe this is just me, but I don't completely trust this to work without losing a certain amount of peak memory performance. I hope they at least leave the option to turn it off, so we can verify the impact it has on a per-application basis.
I think you'd have a hard time making a case that Summit is a cyclical return to any part of the Amiga 500 :)
"fully coherent single address space with all memory available to [all processors]" is a lot easier if there is no virtual memory, no caching, and all processors are on a single shared bus to the same memory chips.
> Single-socket nodes will consist of one CPU and four GPUs, connected by AMD’s custom high bandwidth, low latency coherent Infinity fabric.
The article, at least, only describes coherency in the context of single-socket nodes. I suppose it's ambiguous whether Infinity fabric only connects components on the node or connects nodes to each other, but in fact Infinity fabric is the interconnect used with Zen CPUs, so I think it's pretty clear that in this case it only connects and provides memory coherency for 1xCPU and 4xGPUs.
Performant memory coherency across nodes would be quite an achievement, but not one they seem to have achieved. Rather, it seems they've simply revived the PS4 model.
> In a media briefing ahead of today’s announcement at Oak Ridge, the partners revealed that Frontier will span more than 100 Shasta supercomputer cabinets, each supporting 300 kilowatts of computing.
So 30 megawatts of computing, plus cooling and other supporting services. How do you power something like this? Does ORNL have their own power station (given they have reactor(s) on site)? If power comes from an external station do they coordinate with the station operator when bringing a system like this online?
As has been noted in other comments, we do not have a power station at ORNL. We buy power from TVA at about 5.5 cents per kW hour which in part is because of the locality of the lab to TVA power plants.
TVA recently completed a 210 MW substation on ORNL's campus to better serve our needs. We do not need to coordinate with them for large runs on the machines.
Nice :-) Back in the day in the UK the RAE royal aircraft establishment twinwoods had a direct line to a local power station for their wind tunnels and used to control the speed form the power staion
With that much gear and those kind of loads do you still have a traditional UPS / transfer switch / genset arrangement for everything in the room? If not, how do you manage short duration power outages?
Yep, we have battery-backed generators for UPS and a transfer switch at the 480-V feed that comes into the room but it is not enough to power the compute nodes. The UPS allows cluster management nodes and the parallel filesystem (which is a small cluster by itself) to ride through full outages and other PQE.
Oak ridge national laboratory was built where it is partly because they could get lots of cheap power from the TVA, so probably from that. (TVA is a regional electricity provider that operates a lot of hydro plants.)
For those who are curious, a typical American home uses of order a kilowatt, time-averaged (10,400 kWh per year = 1.2 kW). So 30 MW is roughly the average power usage of a city of 30,000 homes, or 80,000 people, although total capacity will be larger to handle fluctuations.
...and yet, even a machine at this scale, or even 100 times that, could not come close to being on par, in terms of neural/neuron simulation, with that of the human brain.
We are definitely "doing something wrong" when it comes to artificial neural networking; even though are models are much simpler, it still takes an enormous amount of computing power, both in terms of raw CPU as well as actual electrical needs, just to be able to simulate things at a small scale (and if we use more accurate models, based on what we know about the brain and neurons, then at best our simulations can only be run to simulate, over our actual-time, what would be in actually fractions of a second in real-time).
That our brains can do so much using so little power (wattage), with such a high number of nodes and interconnections that dwarf anything we've so far have managed to simulate - it's a bit mind-boggling and humbling.
I just wonder where and what the issue actually is.
Why do our current practical models of a neuron, which are vastly simplified, require so much power to run at scale?
Is the issue related to the fact that they are simplified models, and actual neurons with their complexity are able to do things we don't yet understand or know about?
All of this is also related to back-propagation; such a thing doesn't seem to exist in nature (jury is still out on the theory, though) - so how do biological neural nets "learn"?
If we could eliminate or reduce the need for backpropagation, would that lower our power requirements for artificial network implementations?
As someone who has merely dabbled with artificial neural networks, these questions and conundrums fascinate me, and cause me to attempt to think up potential solutions, however far-fetched.
I highly doubt I will be the one to solve the issue, but I do hope to see it solved within my lifetime.
The main difference between the brain and a CPU is that the brain runs at a much lower, variable clock speed (10-100Hz) to reduce switching costs and makes up for this with extensive pipelining and parallelization when possible. The high node count and necessary connectivity is then possible due to the use of directed self assembly.
At any rate, it is quite likely that the neocortex of the brain simply computes a function(s) recursively upon sensory input (see Chomsky's minimalist program for suggestions on what it could be). What is unclear is what this function is, and how it comes about - for this reason the approach by some has been to attempt to simulate an entire brain to see what it does. But without the necessary abstractions, this will be inherently wasteful and generates nothing new other than validating your experimental data.
Probably current AI is base on FP64, FP32, biological neuron likely be large network of only one or few bits. Once we figure out how to build, use, optimized large network of nodes which only process a few bits each, the power consumption might just go way down.
Someone might also find that kind of network can run a KHz like human brain cells instead of MHz, GHz. The power usage will go down even more.
They might coordinate with TVA for transients (i.e. going from an idle machine to a full-machine run), but in normal operations these systems are at least 50% full. In my experience as a user these machines are more than 80% most of the time. I don't have hard numbers on the average utilization over the last week/month/year (these might even be classified), but you don't by and build such a machine to be idle.
I could find the numbers for two German super computers I have used in the past. SuperMUC (Phase 1 and 2 combined) had "above the desired 85%" utilization in 2017 and Hazel Hen at HLRS in Stuttgart reported a utilization "between 92% and 98%" in 2017.
> When I took a tour of the Oak Ridge Leadership Computing Facility a few years ago, Buddy Bland, who is Project Director, told me he could tell when he comes to the Lab in the morning if they are running the LINPACK benchmark by looking at how much steam is coming out of the cooling towers.
That is true. But very few super computers track power consumption for different codes and do power aware scheduling. SuperMUC and IBM actually do a lot of research on this, because it is a rather new field in HPC.
Most of the super computers today have their own power station on site. I know blue waters at UIUC had one, which I believe caused a power outage at one point.
So - on a more "applies to ordinary mortals" level - the fact that they are going to use all AMD components is intriguing.
In reference to AI, NVidia has things "locked up" with CUDA, versus 2nd cousin AMD's OpenCL.
From what I understand, it is possible to recompile TensorFlow (for instance - not that ORNL will be using TF) for OpenCL - but I don't know how well it works. Personally, I've only used TF with CUDA.
Does this mean we might see greater/better support for OpenCL in the AI realm? Might we seem it become on-par with CUDA because of this collaboration for this HPC?
Or will things stay as-is, at least "down here" in the consumer/business realm of AI hardware and applications? Do things like this trickle down, or are things so customized and/or proprietary for the needs of HPC at ORNL (or elsewhere) that anything to do with AI on this machine will have little to no bearing outside of the lab?
Ultimately, I'd just like to see another choice (a lower cost choice!) for GPU in the world of consumer/enthusiast/hobbyist AI/DL/ML - while today's higher-end GPUs, no matter the manufacturer, tend to be fairly expensive, AMD still has an edge here that make them attractive to users (not to mention the fact that their Linux drivers are open-source, which is also a plus).
I doubt they are going to run much AI on that machine. The national labs mostly run "traditional HPC" workloads such as fluid codes that simulate (magneto)hydrodynamics in one way or another.
They have done that and it got an award because it was new and cool, but that is not where the majority of cycles goes. That is spent on fluid codes such as Raptor, XGC or Flash and to some extend kinetic simulations with codes such as VPIC or quantum codes such as QFCPACK.
I'd tend to assume, with the amount of resources going into this, that the software will be coded at a lower level here than it would be in a typical dev environment and so NVidia's library advantages will be less salient?
"greater than 1.5 exaflops" of performance will likely correspond to greater than 1 Exaflop of sustained performance on HPL (used for the top-500 ranking), making this a likely candidate for the first 'true' exascale computer.
Out of curiosity what makes this an exascale computer and not, say, an AWS or Azure datacenter? Just the fact that they are open about benchmarking #pflops?
Good question. Technically speaking just running HPL on a cluster is sufficient to get it ranked on top-500.
In reality, most HPC sites use high performance interconnects, such as infiniband or a proprietary high performance ethernet. They are also less likely to use virtualization, and every node looks 'bare metal'. The software stacks are very different, from everything between the distributed memory model, compilers, to the system schedulers and diagnostic software.
Nevertheless, there is a great deal of convergence happening between HPC and hyperscale data centers, particularly as hyperscale uses more machine learning, which has a similar flavor to HPC. Many believe that the FANG companies have exaflop capabilities already, but they just aren't well optimized for scientific workloads.
Taking this idea more seriously than intended: is this practical? I've heard of CPU clusters being used for network distributed realtime raytracing. Is there a DirectX / OpenGL API implementation for this?
The call for proposals for INCITE (one of the programs we provide cycles for) is open for 2020 but this would be for Summit not Frontier this time around :)
PCIe provides communication but isn't intended to provide memory coherency. There's a lot of work that goes on in figuring out which cache(s) have a copy of which cache line and figuring out how to resolve conflicting access needs.
Infinity Fabric / HyperTransport is generally lower level and lower latency than PCIe. It's aimed more for use as a front-side bus than a peripheral interconnect. A better analogue would be Intel's QuickPath Interconnect.
Moore's law might have stopped with the clock gains when Denard scaling gave out but we've still got energy efficiency gains. Koomey's law[1] is holding strong. I don't know if it'll get us all the way to Landauer's Limit[2] but I hope so.
It's probably not a great comparison to compare the theoretical numbers of Frontier to the achieved numbers of the green 500. The achieved flops is pretty much always considerably lower than the theoretical flops. Titan is a 27 PFLOPS machine that achieves 17.6 PFLOPS, sequoia is a 20 PFLOPS machine that achieves 17, summit is a 200 PFLOPS machine that achieves 143, ...
~ 37 GFLOPS/W is probably a better projection if we assume (out of nowhere) that the theoretical/achieved ratio of Frontier is comparable to Summit (75%). Still very impressive.
I wonder if that will be the case on Frontier.