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The art of high performance computing

theartofhpc.com

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Re: The art of high performance computing

#61

Post by victotronics »

cogman10 wrote: post #56
LASR wrote: post #48

The hardware / datacenter side of this is equally fascinating. I used to work in AWS, but on the software / services side of things. But now and then, we would crash some talks from the datacenter folks. One key relevation for me was that increasing compute power in DCs is primarily a thermodynamics problem than actual computing. The nodes have become so dense that shipping power in and shipping heat out, with all ki…

It always made me wonder why liquid cooling wasn't more of a thing for datacenters. Water has a massive amount of thermal capacity and can quickly and in bulk be cooled to optimal temperatures. You'd probably still need fans and AC to dissipate heat of non-liquid cooled parts, but for the big energy items like CPUs and GPUs/compute engines, you could ship out huge amounts of heat fairly quickly and directly. I guess…

Immersion cooling is getting big. At the last Supercomputing conference I probably saw at least a dozen vendors of immersion cooling equipment. My datacenter has one cluster with liquid cooling caps over the sockets, and two immersed clusters. The latter two have basins of various degrees of sophistication under them for when they do spring a leak.

Re: The art of high performance computing

#62

Post by bayindirh »

cogman10 wrote: post #56
LASR wrote: post #48

The hardware / datacenter side of this is equally fascinating. I used to work in AWS, but on the software / services side of things. But now and then, we would crash some talks from the datacenter folks. One key relevation for me was that increasing compute power in DCs is primarily a thermodynamics problem than actual computing. The nodes have become so dense that shipping power in and shipping heat out, with all ki…

It always made me wonder why liquid cooling wasn't more of a thing for datacenters. Water has a massive amount of thermal capacity and can quickly and in bulk be cooled to optimal temperatures. You'd probably still need fans and AC to dissipate heat of non-liquid cooled parts, but for the big energy items like CPUs and GPUs/compute engines, you could ship out huge amounts of heat fairly quickly and directly. I guess…

Because it’s complex. Even more complex than “engineered” air.

You need two circuits, and a CDU between them. Coolants needs maintaining. You add antifreeze, biocides, etc.

Air is brute force. It cools everything it touches. Liquid cooling is serialized in a node. Two sockets? Second will be hotter. HBA not making good contact? It’ll overheat.

You add extensive leak detection subsystems, the amount of coolant moving in your primary circuit becomes massive.

Currently you can remove 97% of the heat via liquid (including the PSUs), and it’s cheaper to do so than air, but it’s not “rails, screws, cables, power on”. Air cooled systems can be turned on in a week. Liquid cooled ones take a month.

However, using liquid is mandatory after some point. You can’t cool systems that dense and under that load with air. They’ll melt.

Re: The art of high performance computing

#63

Post by bayindirh »

adev_ wrote: post #60
cogman10 wrote: post #56

Earlier quoted context omitted.

It always made me wonder why liquid cooling wasn't more of a thing for datacenters. Water has a massive amount of thermal capacity and can quickly and in bulk be cooled to optimal temperatures. You'd probably still need fans and AC to dissipate heat of non-liquid cooled parts, but for the big energy items like CPUs and GPUs/compute engines, you could ship out huge amounts of heat fairly quickly and directly. I guess…

> It always made me wonder why liquid cooling wasn't more of a thing for datacenters. Liquid cooling is almost a defacto-standard in data centers in the HPC world. The Top of the TOP500 machines are all liquid cooled. Not by choice, but due to physics constraints. There is a big gap in power density between the HPC world and the usual datacenter-commodity-hardware world. Commodity DS are designed with the assumption…

> we attempted to install a medium size HPC cluster in a well-known commerical Datacenter and network provider. The commercial of the DS almost fall from his chair when we announced the power requirements.

Heh. We tried it too. They didn’t believe that a single node used their entire rack’s budget at first.

Re: The art of high performance computing

#64

Post by leopoldj »

jebarker wrote: post #52

I'm interested in what people think of the approach to teaching C++ used here. Any particular drawbacks? I'm a very experienced Python programmer with some C, C++ and CUDA doing application level research in HPC environments (ML/DL). I'd really like to level up my C++ skills and looking through book 3 it seems aimed exactly at the right level for me - doesn't move too slowly and teaches best practices (per the author…

C++ programmer and educator here. This (volume 3) is well organized good beginner level teaching material. You probably know most of it already.

I was looking for range-based for loop, std::array and std::span and happy to see that they are all there.

Because this book relates to HPC, I'd add a few things: Return Value Optimization, move semantics, and in the recursive function section a note about Tail Call Optimization.

As a beginner level material I can highly recommend it.

Re: The art of high performance computing

#65

Post by dist1ll »

bayindirh wrote: post #53
dist1ll wrote: post #6

It's very interesting how abtracted away HPC sometimes looks from hardware. The books seem to revolve a lot around SPMD programming, algo & DS, task parallelism, synchronization etc, but very little about computer architecture details like supercomputer memory subsystems, high-bandwidth interconnects like CXL, GPU architecture and so on. Are the abstractions and tooling already good enough that you don't need to worr…

HPC admin here, generally serving "long tail of science" researchers. In today's x86_64 hardware, there's no "supercomputer memory subsystem". It's just a glorified NUMA system, and the biggest problem is putting the memory close to your core, i.e. keeping data local in your NUMA node to reduce latencies. Your resource mapping is handled by your scheduler. It knows your hardware, hence it creates a cgroup which satis…

Thanks for the thoughtful comment, pretty fascinating stuff.

> In today's x86_64 hardware, there's no "supercomputer memory subsystem". It's just a glorified NUMA system, and the biggest problem is putting the memory close to your core, i.e. keeping data local in your NUMA node to reduce latencies.

I mean, memory topology varies greatly by uarch (doubly so between vendors). I can't take a routine tuned to Nehalem, run it on Haswell or Skylake and expect it to stay competitive. More generally, different hardware has different bandwidth and latency ratios, which affects software design (e.g. software written for commodity Dell w/ PCIe cards probably won't translate to Cray accelerator grid connected by HPE slingshot). And then there's hardware-specific features like RNICs bypassing DRAM and writing RDMA messages directly into the receiver's cache. So I think that ccNUMA and data locality is not sufficient to reason about memory perf.

Re: The art of high performance computing

#66

Post by s_Hogg »

throwawaaarrgh wrote: post #43
bluedino wrote: post #37

Earlier quoted context omitted.

I started in HPC about 2 years ago on a ~500 node cluster at a Fortune 100 company. I was really just looking for a job where I was doing Linux 100% of the time, and it's been fun so far. But it wasn't what I thought it would be. I guess I expected to be doing more performance oriented work, analyzing numbers and trying to get every last bit of performance out of the cluster. To be honest, they didn't even have any k…

I always found that funny too. A business who needs a powerful computing solution can come up with some amazingly robust stuff, whereas science/research just buys a big mainframe and hopes it works.

I was working in a company that had been spun out of a university until recently and it was shocking how hopeless the researchers were. I've always been critical of how poor the job security in academia is but you'd think it's still too much given how slapdash some of the crap you see is. We basically had to reinvent their product from the ground up, awful.

Re: The art of high performance computing

#67

Post by gopher_space »

bayindirh wrote: post #62
cogman10 wrote: post #56

Earlier quoted context omitted.

It always made me wonder why liquid cooling wasn't more of a thing for datacenters. Water has a massive amount of thermal capacity and can quickly and in bulk be cooled to optimal temperatures. You'd probably still need fans and AC to dissipate heat of non-liquid cooled parts, but for the big energy items like CPUs and GPUs/compute engines, you could ship out huge amounts of heat fairly quickly and directly. I guess…

Because it’s complex. Even more complex than “engineered” air. You need two circuits, and a CDU between them. Coolants needs maintaining. You add antifreeze, biocides, etc. Air is brute force. It cools everything it touches. Liquid cooling is serialized in a node. Two sockets? Second will be hotter. HBA not making good contact? It’ll overheat. You add extensive leak detection subsystems, the amount of coolant moving…

What's this all look like without an atmosphere?

Re: The art of high performance computing

#68

Post by logtempo »

projectileboy wrote: post #54
LASR wrote: post #48

The hardware / datacenter side of this is equally fascinating. I used to work in AWS, but on the software / services side of things. But now and then, we would crash some talks from the datacenter folks. One key relevation for me was that increasing compute power in DCs is primarily a thermodynamics problem than actual computing. The nodes have become so dense that shipping power in and shipping heat out, with all ki…

Seymour Cray used to say this all the way back in the 1970s: his biggest problems were associated with dissipating heat. For the Cray 2 he took an even more dramatic approach: "The Cray-2's unusual cooling scheme immersed dense stacks of circuit boards in a special non-conductive liquid called Fluorinert™" ( https://www.computerhistory.org/revolution/supercomputers/10... )

Few days ago I saw an article passing by,about chips hiting the kw floor.

Re: The art of high performance computing

#69

Post by jebarker »

leopoldj wrote: post #64
jebarker wrote: post #52

I'm interested in what people think of the approach to teaching C++ used here. Any particular drawbacks? I'm a very experienced Python programmer with some C, C++ and CUDA doing application level research in HPC environments (ML/DL). I'd really like to level up my C++ skills and looking through book 3 it seems aimed exactly at the right level for me - doesn't move too slowly and teaches best practices (per the author…

C++ programmer and educator here. This (volume 3) is well organized good beginner level teaching material. You probably know most of it already. I was looking for range-based for loop, std::array and std::span and happy to see that they are all there. Because this book relates to HPC, I'd add a few things: Return Value Optimization, move semantics, and in the recursive function section a note about Tail Call Optimiza…

That's great - thank-you. Assuming I work through this quickly, what resources would you recommend as a follow-on?
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