From 570d69f04b15de23e9558712fa0ee7687025bc3f Mon Sep 17 00:00:00 2001 From: mahxn0 <1262384588@qq.com> Date: Fri, 20 Dec 2024 10:56:58 +0800 Subject: [PATCH] [Docs](mlu-ops): Update bang-c-ops-develop doc. (#1185) --- ...p-Guide.md => BANG-C-OPS-Develop-Guide.md} | 22 +++++++++--------- docs/images/figure4-1.png | Bin 0 -> 105497 bytes docs/images/figure4-2.png | Bin 0 -> 67265 bytes docs/images/figure4-3.png | Bin 0 -> 33932 bytes docs/images/figure4-4.png | Bin 0 -> 62815 bytes docs/images/figure4-5.png | Bin 0 -> 54662 bytes docs/images/hardwarehierarchy.png | Bin 0 -> 54710 bytes docs/images/memorymodel.png | Bin 0 -> 73163 bytes docs/images/pingpong.png | Bin 0 -> 14220 bytes docs/images/pingpong4.png | Bin 0 -> 984296 bytes docs/images/pingpong5.png | Bin 0 -> 45877 bytes docs/images/taskgrid.png | Bin 0 -> 63681 bytes 12 files changed, 11 insertions(+), 11 deletions(-) rename docs/{BANG C OPS-Develop-Guide.md => BANG-C-OPS-Develop-Guide.md} (98%) create mode 100644 docs/images/figure4-1.png create mode 100644 docs/images/figure4-2.png create mode 100644 docs/images/figure4-3.png create mode 100644 docs/images/figure4-4.png create mode 100644 docs/images/figure4-5.png create mode 100644 docs/images/hardwarehierarchy.png create mode 100644 docs/images/memorymodel.png create mode 100644 docs/images/pingpong.png create mode 100644 docs/images/pingpong4.png create mode 100644 docs/images/pingpong5.png create mode 100644 docs/images/taskgrid.png diff --git a/docs/BANG C OPS-Develop-Guide.md b/docs/BANG-C-OPS-Develop-Guide.md similarity index 98% rename from docs/BANG C OPS-Develop-Guide.md rename to docs/BANG-C-OPS-Develop-Guide.md index 7eb995688..d039d7b43 100644 --- a/docs/BANG C OPS-Develop-Guide.md +++ b/docs/BANG-C-OPS-Develop-Guide.md @@ -615,7 +615,7 @@ int main() { #### 3.3.1 抽象硬件模型

- example + example
图 3.1 Cambricon BANG 异构计算平台的抽象硬件模型

@@ -631,7 +631,7 @@ int main() { #### 3.3.2 抽象存储模型

- example + example
图 3.2 Cambricon BANG 抽象存储模型

@@ -670,7 +670,7 @@ int main() { 如下图所示,Inst-Cache 中的一段指令序列顺序开始执行后,经过分发和调度被分配进多个 PIPE 队列, 进入 PIPE 前指令是顺序执行被译码的,进入不同计算或访存队列后由硬件解决寄存器重命名或读写依赖。MLUv03 的 IO-DMA 和 Move-DMA 互相独立, 所以 MLUv03 用户的片上的访存操作和片外访存操作可以无依赖独立执行。

- example + example
图 3.3

@@ -717,7 +717,7 @@ int main() { 下图给出两个多维 Kernel 函数并行展开映射到硬件 MLU Cluster 执行的示意,如果使用整个 MLU Device 时都使用相同任务类型例如都是 Union1 类型的任务, 那么整个设备的利用率会较高,因为硬件调度器看到的并行粒度统一,不会出现 Union2 等待 2 个 MLU Cluster 的情况。

- example + example
图 3.5 MLU Cluster 的并发

@@ -729,7 +729,7 @@ int main() { 用户可以使用 `foo<<<...>>>()` 完成异步的 Kernel 计算,还可以使用 CNRT 或 CNDrv 的异步 Memcpy 实现 H2D/D2D/D2H 的传输, 如上一小结所述,MLU Device 会根据下发的任务类型调度执行一个 Block 或 UnionN,那么在 Host 和 Device 之间就有一个任务队列, 不同硬件支持的队列最大深度是不一样的,用户可以使用 `cnDeviceGetAttribute()` 接口和 `CN_DEVICE_ATTRIBUTE_MAX_QUEUE_COUNT` 来获取。

- example + example
图 3.6 Host 和 Device 的并发

@@ -746,7 +746,7 @@ MLU 架构的并行或并发如前面所述可以分为 MLU Core 、MLU Cluster 如[内部并行指令流水和计算架构抽象](#333-mlu-core-内部并行指令流水和计算架构抽象)所示,MLU Core 内的多个 PIPE 可以异步并行执行,我们将不同 PIPE 的运算或者访存抽象为读和写的话, 即可建立依赖关系,如下图蓝色连线 A/B/C/D/E/F:

- example + example
图 3.7 MLUv03 MLU Core 内多指令流水线同步

@@ -766,7 +766,7 @@ MLU 架构的并行或并发如前面所述可以分为 MLU Core 、MLU Cluster - `__sync_all()` 负责同步一个软件概念的 Union Block Task 的全部 Block Task,无论用户启动的是 Union1、Union2、Union4、UnionX 等联合任务,此函数会同步 UnionX 映射到的全部 X 个 Cluster, 对应的指令伪代码是 `barrier.sync.global ID, (%coreDim + 1) * %clusterDim`

- example + example
图 3.8 MLUv03 MLU Cluster 的同步

@@ -856,7 +856,7 @@ dim.z = 4; 上述配置描述的三维任务网格如下图所示。

- example + example
图 3.9 三维任务网格示意图

@@ -1003,7 +1003,7 @@ UnionN (N=1, 2, 4, 8, ...) 任务表示一个 Kernel 在执行时至少需要占 在指令中,①硬件 I/O(Load&Store) 和 ② Compute 是分别依赖 I/O 队列和 Compute 队列实现的。两个队列可以同时发射指令,发射出去的指令可以并行执行。为了充分利用这种并行优势,编程中引入了流水线的概念。流水线,本质上是利用两份资源,具体地,将 NRAM 空间分为 ping/pong 部分。每部分各占一半。如下图所示:

- example + example
图 4.1 三级流水示意图

@@ -1308,7 +1308,7 @@ UnionN (N=1, 2, 4, 8, ...) 任务表示一个 Kernel 在执行时至少需要占 以 Union1 任务类型为例,四级流水的过程包括,首先由 Memory Core 将数据从 GDRAM 搬运到 SRAM 中,然后再由 coreDim(以4为例)个 MLU Core 分别从 SRAM 搬运部分数据到 NRAM 中进行计算,计算结果由 coreDim 个 MLU Core 搬运回 GDRAM 中,从而实现了四级流水。如下图所示:

- example + example
图 4.2 四级软件流水示意图

@@ -1604,7 +1604,7 @@ UnionN (N=1, 2, 4, 8, ...) 任务表示一个 Kernel 在执行时至少需要占 以 Union1 任务类型为例,常用的五级流水的过程包括,首先由 Memory Core 将数据从 GDRAM 搬运到 SRAM 中,然后再由 coreDim(以 4 为例)个MLU Core 分别从 SRAM 搬运部分数据到 NRAM 中进行计算,计算结果先由 coreDim个 MLU Core 搬运到 SRAM 上,再由 Memory Core 从 SRAM 搬运到 GDRAM 上,从而实现了五级流水。如下图所示:

- example + example
图 4.3 五级软件流水示意图

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