From 7a34692374bb38e423bb6b297156680dd0fd1ec1 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Thu, 14 Dec 2017 21:24:53 +0000 Subject: [PATCH] Deploy to GitHub Pages: c52a0bde2f101e9227ffe4a9bd00ddad8edd2238 --- .../doc/_sources/design/paddle_nccl.md.txt | 65 ++++ develop/doc/design/paddle_nccl.html | 330 +++++++++++++++++ develop/doc/objects.inv | Bin 3644 -> 3669 bytes develop/doc/searchindex.js | 2 +- .../doc_cn/_sources/design/paddle_nccl.md.txt | 65 ++++ develop/doc_cn/design/paddle_nccl.html | 332 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 4189 -> 4214 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 794 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/paddle_nccl.md.txt create mode 100644 develop/doc/design/paddle_nccl.html create mode 100644 develop/doc_cn/_sources/design/paddle_nccl.md.txt create mode 100644 develop/doc_cn/design/paddle_nccl.html diff --git a/develop/doc/_sources/design/paddle_nccl.md.txt b/develop/doc/_sources/design/paddle_nccl.md.txt new file mode 100644 index 00000000000..c7dac70998a --- /dev/null +++ b/develop/doc/_sources/design/paddle_nccl.md.txt @@ -0,0 +1,65 @@ +# Design Doc: NCCL support in Paddle Fluid + +## Abstract + +This Design Doc refers to the NCCL feature in paddle. We propose an approach to support NCCL library both on a single machine and multiple machines. We wrapper the NCCL primitives `Broadcast`, `Allreduce`, `Reduce` as operators to utilize Multi-GPU powers in one script. + + +## Motivation + +[NCCL](https://developer.nvidia.com/nccl) is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. With NCCL library, we can easily accelerate the training in parallel. + +- Pros +1. easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. +1. high performance in NVIDIA GPUs. +1. MPI like primitives, which have low learning cost for users. + +- Cons +1. Only design for NVIDIA GPUs, not a general multi-device solution. +1. Although NCCL1 is opensourced under BSD license, but NCCL2 is not opensourced anymore. + +At the beginning of training, the framework needs to distribute the same parameters to every GPU, and merge the gradients at any time user interests. + +As a result, during training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the operator with correct place information. + +Besides, it needs interfaces to synchronize model update with each different GPU Cards. + +## Implementation + +As mentioned above, we wrap the NCCL routines as several kinds of operators. Need to note that NCCL need to create Communicator between gpu at the beginning, so there is a NCCLInit operator created. + +### Transpiler + +To be compatible with [parameter server design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md), the transpiler compiles the user defined operation graph into sub-graphs to be executed on different devices. + +1. The user-defined model will be a single device program + +2. Broadcast/Reduce operators between GPUs will be inserted into the program, even for the multi-node, may insert the `Send`, `Recv` operator. + + *Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, [Send, Recv](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md#graph-converter) in multiple machines* + + + +After compiling, the graph as shows + + + +Operators are added to the sub-graphs. Every GPU assigned a role of `rank0`, `rank1` etc. + +- **Broadcast**. Broadcast operator distribute initialized parameter to all the GPUs from the GPU who owns it. e.g. from`rank0` GPU. +- **AllReduce**. AllReduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU. + +Need to notice that AllReduce operator force GPUs synchronized at that point. The whole training process in asynchronous or synchronous mode depends on the AllReduce point in the graph. + +As it shown in the picture, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. + +- **AllReduce** + Need to note that our AllReduce operator is a ring-base AllReduce implementation. If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, NCCL2 built-in AllReduce will only utilize the communicating resource during synchronization, then update the gradient will be a subsequent phase. In fact, we can amortize the update gradient time cost into the communicating phase. The process is +1. Every parameter has its root card. That card will responsible for aggregating the gradients from GPUs. +2. The whole model's parameter will be hashed to different root card, ensure the load balance between GPUs. +3. Logically neighberhood card will start send parameter to the next one. After one round, the parameter main card will aggregate the full gradients. +4. Then the root card will optimize the parameter. +5. This parameter card will send its optimized result to its neighberhood, then the neighberhood will send parameter to its next one. +6. Finish the sychronization round. + +The total time cost will be 2 * (n-1) * per-parameter-send-time, we reach the goal of amortize the upgrade time into communicating phase. diff --git a/develop/doc/design/paddle_nccl.html b/develop/doc/design/paddle_nccl.html new file mode 100644 index 00000000000..7a50efc1868 --- /dev/null +++ b/develop/doc/design/paddle_nccl.html @@ -0,0 +1,330 @@ + + + + + + + + + + + Design Doc: NCCL support in Paddle Fluid — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: NCCL support in Paddle Fluid
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Design Doc: NCCL support in Paddle Fluid

+
+

Abstract

+

This Design Doc refers to the NCCL feature in paddle. We propose an approach to support NCCL library both on a single machine and multiple machines. We wrapper the NCCL primitives Broadcast, Allreduce, Reduce as operators to utilize Multi-GPU powers in one script.

+
+
+

Motivation

+

NCCL is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. With NCCL library, we can easily accelerate the training in parallel.

+
    +
  • Pros
    • +
+
    +
  1. easily plug-in with NCCL2 library.
    2. +
  2. high performance in NVIDIA GPUs.
    3. +
  3. MPI like primitives, which have low learning cost for users.
    4. +
+
    +
  • Cons
    • +
+
    +
  1. Only design for NVIDIA GPUs, not a general multi-device solution.
    2. +
  2. Although NCCL1 is opensourced under BSD license, but NCCL2 is not opensourced anymore.
    3. +
+

At the beginning of training, the framework needs to distribute the same parameters to every GPU, and merge the gradients at any time user interests.

+

As a result, during training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the operator with correct place information.

+

Besides, it needs interfaces to synchronize model update with each different GPU Cards.

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+
+

Implementation

+

As mentioned above, we wrap the NCCL routines as several kinds of operators. Need to note that NCCL need to create Communicator between gpu at the beginning, so there is a NCCLInit operator created.

+
+

Transpiler

+

To be compatible with parameter server design doc, the transpiler compiles the user defined operation graph into sub-graphs to be executed on different devices.

+
    +

  1. The user-defined model will be a single device program

    +
    2. +

  2. Broadcast/Reduce operators between GPUs will be inserted into the program, even for the multi-node, may insert the Send, Recv operator.

    +

    Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, Send, Recv in multiple machines

    +

    +
    3. +
+

After compiling, the graph as shows

+

+

Operators are added to the sub-graphs. Every GPU assigned a role of rank0, rank1 etc.

+
    +
  • Broadcast. Broadcast operator distribute initialized parameter to all the GPUs from the GPU who owns it. e.g. fromrank0 GPU.
    • +
  • AllReduce. AllReduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU.
    • +
+

Need to notice that AllReduce operator force GPUs synchronized at that point. The whole training process in asynchronous or synchronous mode depends on the AllReduce point in the graph.

+

As it shown in the picture, when each GPU compute the gradient of W, followed with a AllReduce operator, accumulate the dW to full batch of data, then run the optimize process individually and apply the gradient to its W.

+
    +
  • AllReduce +Need to note that our AllReduce operator is a ring-base AllReduce implementation. If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, NCCL2 built-in AllReduce will only utilize the communicating resource during synchronization, then update the gradient will be a subsequent phase. In fact, we can amortize the update gradient time cost into the communicating phase. The process is
    • +
+
    +
  1. Every parameter has its root card. That card will responsible for aggregating the gradients from GPUs.
    2. +
  2. The whole model’s parameter will be hashed to different root card, ensure the load balance between GPUs.
    3. +
  3. Logically neighberhood card will start send parameter to the next one. After one round, the parameter main card will aggregate the full gradients.
    4. +
  4. Then the root card will optimize the parameter.
    5. +
  5. This parameter card will send its optimized result to its neighberhood, then the neighberhood will send parameter to its next one.
    6. +
  6. Finish the sychronization round.
    7. +
+

The total time cost will be 2 * (n-1) * per-parameter-send-time, we reach the goal of amortize the upgrade time into communicating phase.

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+ © Copyright 2016, PaddlePaddle developers. + +

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+ Built with Sphinx using a theme provided by Read the Docs. + +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/paddle_nccl.md.txt b/develop/doc_cn/_sources/design/paddle_nccl.md.txt new file mode 100644 index 00000000000..c7dac70998a --- /dev/null +++ b/develop/doc_cn/_sources/design/paddle_nccl.md.txt @@ -0,0 +1,65 @@ +# Design Doc: NCCL support in Paddle Fluid + +## Abstract + +This Design Doc refers to the NCCL feature in paddle. We propose an approach to support NCCL library both on a single machine and multiple machines. We wrapper the NCCL primitives `Broadcast`, `Allreduce`, `Reduce` as operators to utilize Multi-GPU powers in one script. + + +## Motivation + +[NCCL](https://developer.nvidia.com/nccl) is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. With NCCL library, we can easily accelerate the training in parallel. + +- Pros +1. easily plug-in with [NCCL2](https://developer.nvidia.com/nccl) library. +1. high performance in NVIDIA GPUs. +1. MPI like primitives, which have low learning cost for users. + +- Cons +1. Only design for NVIDIA GPUs, not a general multi-device solution. +1. Although NCCL1 is opensourced under BSD license, but NCCL2 is not opensourced anymore. + +At the beginning of training, the framework needs to distribute the same parameters to every GPU, and merge the gradients at any time user interests. + +As a result, during training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the operator with correct place information. + +Besides, it needs interfaces to synchronize model update with each different GPU Cards. + +## Implementation + +As mentioned above, we wrap the NCCL routines as several kinds of operators. Need to note that NCCL need to create Communicator between gpu at the beginning, so there is a NCCLInit operator created. + +### Transpiler + +To be compatible with [parameter server design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md), the transpiler compiles the user defined operation graph into sub-graphs to be executed on different devices. + +1. The user-defined model will be a single device program + +2. Broadcast/Reduce operators between GPUs will be inserted into the program, even for the multi-node, may insert the `Send`, `Recv` operator. + + *Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, [Send, Recv](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/ops/dist_train.md#graph-converter) in multiple machines* + + + +After compiling, the graph as shows + + + +Operators are added to the sub-graphs. Every GPU assigned a role of `rank0`, `rank1` etc. + +- **Broadcast**. Broadcast operator distribute initialized parameter to all the GPUs from the GPU who owns it. e.g. from`rank0` GPU. +- **AllReduce**. AllReduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU. + +Need to notice that AllReduce operator force GPUs synchronized at that point. The whole training process in asynchronous or synchronous mode depends on the AllReduce point in the graph. + +As it shown in the picture, when each GPU compute the gradient of `W`, followed with a `AllReduce` operator, accumulate the `dW` to full batch of data, then run the optimize process individually and apply the gradient to its `W`. + +- **AllReduce** + Need to note that our AllReduce operator is a ring-base AllReduce implementation. If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, NCCL2 built-in AllReduce will only utilize the communicating resource during synchronization, then update the gradient will be a subsequent phase. In fact, we can amortize the update gradient time cost into the communicating phase. The process is +1. Every parameter has its root card. That card will responsible for aggregating the gradients from GPUs. +2. The whole model's parameter will be hashed to different root card, ensure the load balance between GPUs. +3. Logically neighberhood card will start send parameter to the next one. After one round, the parameter main card will aggregate the full gradients. +4. Then the root card will optimize the parameter. +5. This parameter card will send its optimized result to its neighberhood, then the neighberhood will send parameter to its next one. +6. Finish the sychronization round. + +The total time cost will be 2 * (n-1) * per-parameter-send-time, we reach the goal of amortize the upgrade time into communicating phase. diff --git a/develop/doc_cn/design/paddle_nccl.html b/develop/doc_cn/design/paddle_nccl.html new file mode 100644 index 00000000000..c0946063f4a --- /dev/null +++ b/develop/doc_cn/design/paddle_nccl.html @@ -0,0 +1,332 @@ + + + + + + + + + + + Design Doc: NCCL support in Paddle Fluid — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: NCCL support in Paddle Fluid
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Design Doc: NCCL support in Paddle Fluid

+
+

Abstract

+

This Design Doc refers to the NCCL feature in paddle. We propose an approach to support NCCL library both on a single machine and multiple machines. We wrapper the NCCL primitives Broadcast, Allreduce, Reduce as operators to utilize Multi-GPU powers in one script.

+
+
+

Motivation

+

NCCL is a NVIDIA library support Multi-GPU communicating and optimized for NVIDIA GPUs, it provides routines such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, that can achieve high bandwidth over PCIe and NVLink high-speed interconnect. With NCCL library, we can easily accelerate the training in parallel.

+
    +
  • Pros
    • +
+
    +
  1. easily plug-in with NCCL2 library.
    2. +
  2. high performance in NVIDIA GPUs.
    3. +
  3. MPI like primitives, which have low learning cost for users.
    4. +
+
    +
  • Cons
    • +
+
    +
  1. Only design for NVIDIA GPUs, not a general multi-device solution.
    2. +
  2. Although NCCL1 is opensourced under BSD license, but NCCL2 is not opensourced anymore.
    3. +
+

At the beginning of training, the framework needs to distribute the same parameters to every GPU, and merge the gradients at any time user interests.

+

As a result, during training, we need the operations of peer to peer copy between different GPUs, aggregating gradients/parameters from GPUs, and broadcasting parameters to GPUs. Every GPU only need to run the operator with correct place information.

+

Besides, it needs interfaces to synchronize model update with each different GPU Cards.

+
+
+

Implementation

+

As mentioned above, we wrap the NCCL routines as several kinds of operators. Need to note that NCCL need to create Communicator between gpu at the beginning, so there is a NCCLInit operator created.

+
+

Transpiler

+

To be compatible with parameter server design doc, the transpiler compiles the user defined operation graph into sub-graphs to be executed on different devices.

+
    +

  1. The user-defined model will be a single device program

    +
    2. +

  2. Broadcast/Reduce operators between GPUs will be inserted into the program, even for the multi-node, may insert the Send, Recv operator.

    +

    Broadcast, AllReduce in a single machine. And Broadcast, AllReduce, Send, Recv in multiple machines

    +

    +
    3. +
+

After compiling, the graph as shows

+

+

Operators are added to the sub-graphs. Every GPU assigned a role of rank0, rank1 etc.

+
    +
  • Broadcast. Broadcast operator distribute initialized parameter to all the GPUs from the GPU who owns it. e.g. fromrank0 GPU.
    • +
  • AllReduce. AllReduce operator synchronizes parameters/gradients between GPUs. AllReduce implemented in the Ring-Based communicating method, avoid of the bottle neck in a single GPU.
    • +
+

Need to notice that AllReduce operator force GPUs synchronized at that point. The whole training process in asynchronous or synchronous mode depends on the AllReduce point in the graph.

+

As it shown in the picture, when each GPU compute the gradient of W, followed with a AllReduce operator, accumulate the dW to full batch of data, then run the optimize process individually and apply the gradient to its W.

+
    +
  • AllReduce +Need to note that our AllReduce operator is a ring-base AllReduce implementation. If we use the NCCL2 AllReduce primitive, every GPU optimized full batch of data, wasted (n-1) GPU compute resources. In addition, NCCL2 built-in AllReduce will only utilize the communicating resource during synchronization, then update the gradient will be a subsequent phase. In fact, we can amortize the update gradient time cost into the communicating phase. The process is
    • +
+
    +
  1. Every parameter has its root card. That card will responsible for aggregating the gradients from GPUs.
    2. +
  2. The whole model’s parameter will be hashed to different root card, ensure the load balance between GPUs.
    3. +
  3. Logically neighberhood card will start send parameter to the next one. After one round, the parameter main card will aggregate the full gradients.
    4. +
  4. Then the root card will optimize the parameter.
    5. +
  5. This parameter card will send its optimized result to its neighberhood, then the neighberhood will send parameter to its next one.
    6. +
  6. Finish the sychronization round.
    7. +
+

The total time cost will be 2 * (n-1) * per-parameter-send-time, we reach the goal of amortize the upgrade time into communicating phase.

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+ © Copyright 2016, PaddlePaddle developers. + +

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+ Built with Sphinx using a theme provided by Read the Docs. + +
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