Messages in this thread | | | From | Jesper Dangaard Brouer <> | Date | Wed, 26 Jul 2023 19:36:14 +0200 | Subject | Re: Memory providers multiplexing (Was: [PATCH net-next v4 4/5] page_pool: remove PP_FLAG_PAGE_FRAG flag) |
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On 25/07/2023 06.04, Mina Almasry wrote: > On Mon, Jul 24, 2023 at 7:56 AM Jesper Dangaard Brouer > <jbrouer@redhat.com> wrote: >> >> >> >> On 17/07/2023 03.53, Mina Almasry wrote: >>> On Fri, Jul 14, 2023 at 8:55 AM Jason Gunthorpe <jgg@ziepe.ca> wrote: >>>> >>>> On Fri, Jul 14, 2023 at 07:55:15AM -0700, Mina Almasry wrote: >>>> >>>>> Once the skb frags with struct new_abstraction are in the TCP stack, >>>>> they will need some special handling in code accessing the frags. But >>>>> my RFC already addressed that somewhat because the frags were >>>>> inaccessible in that case. In this case the frags will be both >>>>> inaccessible and will not be struct pages at all (things like >>>>> get_page() will not work), so more special handling will be required, >>>>> maybe. >>>> >>>> It seems sort of reasonable, though there will be interesting concerns >>>> about coherence and synchronization with generial purpose DMABUFs that >>>> will need tackling. >>>> >>>> Still it is such a lot of churn and weridness in the netdev side, I >>>> think you'd do well to present an actual full application as >>>> justification. >>>> >>>> Yes, you showed you can stick unordered TCP data frags into GPU memory >>>> sort of quickly, but have you gone further with this to actually show >>>> it is useful for a real world GPU centric application? >>>> >>>> BTW your cover letter said 96% utilization, the usual server >>>> configuation is one NIC per GPU, so you were able to hit 1500Gb/sec of >>>> TCP BW with this? >>>> >>> >>> I do notice that the number of NICs is missing from our public >>> documentation so far, so I will refrain from specifying how many NICs >>> are on those A3 VMs until the information is public. But I think I can >>> confirm that your general thinking is correct, the perf that we're >>> getting is 96.6% line rate of each GPU/NIC pair, >> >> What do you mean by 96.6% "line rate". >> Is is the Ethernet line-rate? >> > > Yes I believe this is the ethernet line-rate. I.e. the 200 Gbits/sec > that my NICs run. > >> Is the measured throughput the measured TCP data "goodput"? > > Yes, it is goodput. Roughly I believe we add up the return values of > recvmsg() and divide that number by time (very roughly, I think). > >> Assuming >> - MTU 1500 bytes (1514 on wire). >> - Ethernet header 14 bytes >> - IP header 20 bytes >> - TCP header 20 bytes >> >> Due to header overhead the goodput will be approx 96.4%. >> - (1514-(14+20+20))/1514 = 0.9643 >> - (Not taking Ethernet interframe gap into account). >> >> Thus, maybe you have hit Ethernet wire line-rate already? > > My MTU is 8244 actually, which gives me 8192 mss/payload for my > connections. By my math the theoretical max would be 1 - 52/8244 = > ~99.3%. So it looks like I'm dropping ~3% line rate somewhere in the > implementation. >
Close enough, my math I would have added the 4 byte FCS checksum (1-56/8244), but it makes no real difference at MTU 8244.
>> >>> and scales linearly >>> for each NIC/GPU pair we've tested with so far. Line rate of each >>> NIC/GPU pair is 200 Gb/sec. >>> >>> So if we have 8 NIC/GPU pairs we'd be hitting 96.6% * 200 * 8 = 1545 GB/sec. >> >> Lets keep our units straight. >> Here you mean 1545 Gbit/sec, which is 193 GBytes/s >> > > Yes! Sorry! I definitely meant 1545 Gbits/sec, sorry! > >>> If we have, say, 2 NIC/GPU pairs, we'd be hitting 96.6% * 200 * 2 = 384 GB/sec >> >> Here you mean 384 Gbit/sec, which is 48 GBytes/sec. >> > > Correct again! > >>> ... >>> etc. >>> >> >> These massive throughput numbers are important, because they *exceed* >> the physical host RAM/DIMM memory speeds. >> >> This is the *real argument* why software cannot afford to do a single >> copy of the data from host-RAM into GPU-memory, because the CPU memory >> throughput to DRAM/DIMM are insufficient. >> >> My testlab CPU E5-1650 have 4 DIMM slots DDR4 >> - Data Width: 64 bits (= 8 bytes) >> - Configured Memory Speed: 2400 MT/s >> - Theoretical maximum memory bandwidth: 76.8 GBytes/s (2400*8*4) >> >> Even the theoretical max 76.8 GBytes/s (614 Gbit/s) is not enough for >> the 193 GBytes/s or 1545 Gbit/s (8 NIC/GPU pairs). >> >> When testing this with lmbench tool bw_mem, the results (below >> signature) are in the area 14.8 GBytes/sec (118 Gbit/s), as soon as >> exceeding L3 cache size. In practice it looks like main memory is >> limited to reading 118 Gbit/s *once*. (Mina's NICs run at 200 Gbit/s) >>
Some more insights. I couldn't believe this (single CPU) test was so far from the theoretical max (76.8 vs. 14.8 GBytes/s). This smells like a per CPU core limitation. The lmbench tool bw_mem have an option for parallelism (-P) for testing this. My testlab CPU only have 6 cores (as I have disabled HT).
Testing on more CPU cores show an increase in scaling mem bandwidth:
Cores 1 = 15.0 GB/s - scale: 1.00 (one core as scale point) Cores 2 = 26.9 GB/s - scale: 1.79 Cores 3 = 36.3 GB/s - scale: 2.42 Cores 4 = 44.0 GB/s - scale: 2.93 Cores 5 = 48.9 GB/s - scale: 3.26 Cores 6 = 49.4 GB/s - scale: 3.29
Thus, the practical test show CPU memory DIMM read bandwidth scales to 49.4 GB/s (395.2 Gbit/s), so there is still hope of 400Gbit/s devices, when utilizing more CPU cores.
I don't have a clear explanation why there is a limit per core.
The [toplev] tool with (bw_mem -P2) says: Backend_Bound = 90.7% of the time. Backend_Bound.Memory_Bound = 71.8% of these 90.7% Backend_Bound.Core_Bound = remaining 18.9%
Backend_Bound.Memory_Bound is split into two main "stalls": Backend_Bound.Memory_Bound.L3_Bound = 7.9% Backend_Bound.Memory_Bound.DRAM_Bound = 58.5%
[toplev] https://github.com/andikleen/pmu-tools
>> Given DDIO can deliver network packets into L3, I also tried to figure >> out what the L3 read bandwidth, which I measured to be 42.4 GBits/sec >> (339 Gbit/s), in hopes that it would be enough, but it was not. >>
The memory bandwidth to L3 cache scales up per CPU core:
Cores 1 = 42.35 GB/s = scale: 1.00 (one core as scale point) Cores 2 = 86.38 GB/s = scale: 2.04 Cores 3 = 126.96 GB/s = scale: 3.00 Cores 4 = 168.48 GB/s = scale: 3.98 Cores 5 = 211.77 GB/s = scale: 5.00 Cores 6 = 244.95 GB/s = scale: 5.78
Nice to see how well this scales up per core. Fairly impressive total max L3 bandwidth of 244.95 GB/s (1959.6 Gbit/s).
>> > > Yes, avoiding any memory speed bottleneck as you note is important, > but the second point mentioned in my cover letter is also impactful: > > " Alleviate PCIe BW pressure, by limiting data transfer to the lowest level > of the PCIe tree, compared to traditional path which sends data through the > root complex." >
This is a good and important point.
> Depending on the hardware, this is a bottleneck that we avoid with > device memory TCP. NIC/GPU copies occupy the PCIe link bandwidth. In a > hierarchy like this: > > root complex > | (uplink) > PCIe switch > / \ > NIC GPU > > I believe the uplink from the PCIe switch to the root complex is used > up 2 times for TX and 2 times for RX if the data needs to go through > host memory: > > RX: NIC -> root complex -> GPU > TX: GPU -> root complex -> NIC > > With device memory TCP, and enabling PCI P2P communication between the > devices under the same PCIe switch, the payload flows directly from/to > the NIC/GPU through the PCIe switch, and the payload never goes to the > root complex, alleviating pressure/bottleneck on that link between the > PCIe switch/root complex. I believe this is a core reason we're able > to scale throughput linearly with NIC/GPU pairs, because we don't > stress share uplink connections and all the payload data transfer > happens beneath the PCIe switch. >
Good points, and I guess this is what Jason was hinting to. And illustrated in this picture[1] (I googled):
[1] https://www.servethehome.com/wp-content/uploads/2022/08/HC34-NVIDIA-DGX-H100-Data-network-configuration.jpg
The GPUs "internally" have switched nvlink connections. As Jason said, these nvlinks have an impressive bandwidth[2] of 900GB/s (7200 Gbit/s).
[2] https://www.nvidia.com/en-us/data-center/nvlink/
>> --Jesper >> (data below signature) >>
Added raw commands and data below.
>> CPU under test: >> >> $ cat /proc/cpuinfo | egrep -e 'model name|cache size' | head -2 >> model name : Intel(R) Xeon(R) CPU E5-1650 v4 @ 3.60GHz >> cache size : 15360 KB >> >> >> Providing some cmdline outputs from lmbench "bw_mem" tool. >> (Output format is "%0.2f %.2f\n", megabytes, megabytes_per_second) >>
Running bw_mem with parallelism utilizing more cores:
$ /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P1 256m rd 268.44 15015.69
$ /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P2 256m rd 268.44 26896.42
$ /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P3 256m rd 268.44 36347.36
$ /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P4 256m rd 268.44 44073.72
$ /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P5 256m rd 268.44 48872.02
$ /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P6 256m rd 268.44 49426.76
>> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 256M rd >> 256.00 14924.50 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 256M wr >> 256.00 9895.25 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 256M rdwr >> 256.00 9737.54 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 256M bcopy >> 256.00 12462.88 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 256M bzero >> 256.00 14869.89 >> >> >> Next output shows reducing size below L3 cache size, which shows an >> increase in speed, likely the L3 bandwidth. >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 64M rd >> 64.00 14840.58 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 32M rd >> 32.00 14823.97 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 16M rd >> 16.00 24743.86 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 8M rd >> 8.00 40852.26 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 4M rd >> 4.00 42545.65 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 2M rd >> 2.00 42447.82 >> >> $ taskset -c 2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem 1M rd >> 1.00 42447.82 >> >
Tests for testing L3 per core scaling.
$ taskset -c 0 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P1 512K rd 0.512000 42357.43
$ taskset -c 0-1 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P2 512K rd 0.512000 86380.09
$ taskset -c 0-2 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P3 512K rd 0.512000 126960.94
$ taskset -c 0-3 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P4 512K rd 0.512000 168485.49
$ taskset -c 0-4 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P5 512K rd 0.512000 211770.67
$ taskset -c 0-5 /usr/lib/lmbench/bin/x86_64-linux-gnu/bw_mem -W2 -N4 -P6 512K rd 0.512000 244959.35
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