Negotiation

There are two parameters which are negotiated between peers on connection creation: 

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After looking at the 2.7 code base, it appears that the only real use of map_on_demand was to use it had two uses:

1. Used as a flag to

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1. turn on the use of FMR or PMR. It wouldn't really matter if it was set to 1 or 256, since again in the FMR case rd_nfrags == 1

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1. .
2. Used to allocate the maximum size of work request queue.

NOTE: init_qp_attr->cap.max_send_wr is set to IBLND_SEND_WRS(conn) on connection creation. That macro derives its value from ibc_max_frags which reflects the negotiated value based on the configured map_on_demand.

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Conclusion on map_on_demand

It appears the intended usage The main purpose of map_on_demand  is to control the maximum number of RDMA fragments transferred. However, when calculating the rd_nfrags in kiblnd_map_tx(), there is no consideration given to the negotiated max_frags value. The underlying assumption in the code then is that if rd_nfrags exceeds the number of negotiated max_frags, we can use FMR/FastReg which maps all the fragments into 1 FMR/FastReg fragment and if we are using FMR/Fast Reg there is no real impact to this tunable. An assumption now broken due to https://review.whamcloud.com/29290/. This patch handles gaps in the fragments by describe each fragment in the RD as a zero based address; however, there could be up to 256 fragments (on x86_64), and if the negotiated max_frags is less than that, then write will fail.Given the usage of map_on_demand described above I find it difficult to understand the necessity of having this tunable. It appears to only complicate the code without adding any significant functionality. is to negotiate the size of the work requests queue size on the opposite sides of the QP. By setting it to, for example 32, the behavior would be to use global memory regions (for RHEL7.3 and earlier) for RDMAing buffers which have < 32 fragments or use FMR/FastReg for buffers that have >= 32 fragments. When using FMR we need only 1 WR for RDMA transfer message. This is true because we map the pages to the fmr pool using: ib_fmr_pool_map_phys(), which maps the list of pages to a FMR region, which requires only 1 WR to transfer.

When using FastReg we need 1 for RDMA transfer, 1 for map operation and 1 for invalidate operation, so 3 in total

The benefit, therefore, that map-on-demand provides is the ability to reduce the size of the qp send work requests queue.

However, given the o2iblnd's backwards compatibility requirements, we need to be able to interface with older Lustre versions which use up to 256 fragments. Therefore we decided to remove the map-on-demand configuration and default it to 256. Look below for the proposed solution.

This has the advantage of reducing the complexity of the code; however, in many cases it would consume more memory than needed. This has been observed on OPA using TID-RDMA. Look at 

Proposal

Overview

The way the RMDA write is done in the o2iblnd is as follows:

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1. Remove the ability to configure map-on-demand via tunables or lnetctl/YAML
2. Default the max_send_wr to a multiple of a constant: 256
3. Keep the ability to dial down the number of fragments if the peer supports lower number of fragments. I still don't think there is any actual need to set max_send_wr to anything less than a multiple of 256.
   1. The underlying assumption in the code was that FMR and FastReg both used only 1 fragment, which is no longer the case. If the number of fragments of the message is greater than the number of fragments supported by the peer (or the connection) what should we do? Only option is to divide that into multiple TXs. I contacted Doug Ledford from Redback to see if there is a way to handle gaps in the buffers with FMR on MLX4. If we're able to do that, then it will greatly reduce the complexity of the code.
4. Optimize the case where all the fragments have no gaps so that in the FMR case we only end up setting rd_nfrags to 1. This will reduce the resource usage on the card; less work requests
5. In the case of gaps, then flag the RDMA write as requiring gaps, and when it comes time to map it, check that the connection can support the number of gaps, and if it doesn't then fail with a clear message suggesting that peer set the map-on-demand value to 256.
6. Document the interactions between the ko2iblnd module parameters. Currently there is a spider web of dependencies between the different parameters. Each dependency needs to be justified and documented and removed if it's unnecessary.
7. Create a simple calculator to calculate the impact of changing the parameters.
   1. For example if you set concurrent_sends to a value X, then how many work requests will be created?
      1. This will be handy to easily understand the cluster configuration without having to go through the pain of re-examining the code.

Ticket tracking changes

o2iblnd Calculator

I created a calculator for the o2iblnd tunables. Given two peers with the tunables, it calculates any adjustments to the tunables that will be performed by the o2iblnd, and calculates several connection attributes, that might be of interest.

Image Added

Image Added

Kernel version pivots on 693, which is the RHEL7.4 release. This is significant because in this release there is no more support for global memory regions, which impacts the calculations.

Any version below 693 will use the calculations assuming that global memory regions is supported.

The tool is written in python. Download here.

Python Requirements

• [setuptools] (https://pypi.python.org/pypi/setuptools) - yum install python-setuptools
• Python 2.7
• pysettings - pip install git+https://github.com/UmSenhorQualquer/pysettings.git
• PyQt4 - yum install pyqt4-dev-tools python-qt4
• PyOpenGL [Optional - Only used by some Controls] - yum install python-opengl
• VisVis [Optional - Only used by some Controls] - pip install visvis

Refer to http://pyforms.readthedocs.io/en/latest/ for more details.

Running it

tar -zxvf lustre_2_10_54_o2iblnd_calc.tar.gz
cd lustre_2_10_54
python o2iblnd_tun_gui.py

Even looking at o2iblnd_tun_calc.py makes it simpler to understand how the different tunables impact each other.