Array Partition¶
This is a simple example of matrix multiplication (Row x Col) to demonstrate how to achieve better performance by array partitioning, using HLS kernel in Vitis Environment.
KEY CONCEPTS: Kernel Optimization, HLS C Kernel, Array Partition
KEYWORDS: #pragma HLS ARRAY_PARTITION, complete
This example demonstrates how array partition in HLS kernel can help
to improve the performance. In this example matrix multiplication
functionality is used to showcase the benefit of array partition. Design
contains two kernels “matmul” a simple matrix multiplication and
“matmul_partition” a matrix multiplication implementation using array
partition.
#pragma HLS array partition is used to partition an array into
multiple smaller arrays or memories. Arrays can be partitioned in three
ways, cyclic, block and complete. In this example,
complete partition is used to partition one of the dimension of
local Matrix array as below
int B[MAX_SIZE][MAX_SIZE];
int C[MAX_SIZE][MAX_SIZE];
#pragma HLS ARRAY_PARTITION variable = B dim = 2 complete
#pragma HLS ARRAY_PARTITION variable = C dim = 2 complete
This array partition helps design to access 2nd dimension of both Matrix B and C concurrently to reduce the overall latency.
To see the benefit of array partition, user can look into system estimate report and see overall latency. Latency Information of normal matmul kernel (without partition):
Compute Unit Kernel Name Module Name Start Interval Best (cycles) Avg (cycles) Worst (cycles) Best (absolute) Avg (absolute) Worst (absolute)
------------ ----------- ----------- -------------- ------------- ------------ -------------- --------------- -------------- ----------------
matmul_1 matmul matmul 2068 ~ 3052 2067 2559 3051 6.889 us 8.529 us 9.526 us
Latency Information for matrix multiplication for kernel with partition:
Compute Unit Kernel Name Module Name Start Interval Best (cycles) Avg (cycles) Worst (cycles) Best (absolute) Avg (absolute) Worst (absolute)
------------------ ---------------- ---------------- -------------- ------------- ------------ -------------- --------------- -------------- ----------------
matmul_partition_1 matmul_partition matmul_partition 277 ~ 1260 276 768 1259 0.920 us 2.560 us 4.196 us
Example generates the following information as output when ran on Alevo U250 Card:
Found Platform
Platform Name: Xilinx
INFO: Reading ./build_dir.hw.xilinx_u250_gen3x16_xdma_3_1_202020_1/matmul.xclbin
Loading: './build_dir.hw.xilinx_u250_gen3x16_xdma_3_1_202020_1/matmul.xclbin'
|-------------------------+-------------------------|
| Kernel | Wall-Clock Time (ns) |
|-------------------------+-------------------------|
| matmul: | 6826 |
| matmul: partition | 853 |
|-------------------------+-------------------------|
| Speedup | 8.002345 |
|-------------------------+-------------------------|
Note: Wall Clock Time is meaningful for real hardware execution only, not for emulation.
Please refer to profile summary for kernel execution time for hardware emulation.
TEST PASSED
EXCLUDED PLATFORMS:
All NoDMA Platforms, i.e u50 nodma etc
DESIGN FILES¶
Application code is located in the src directory. Accelerator binary files will be compiled to the xclbin directory. The xclbin directory is required by the Makefile and its contents will be filled during compilation. A listing of all the files in this example is shown below
src/host.cpp
src/matmul.cpp
src/matmul_partition.cpp
Access these files in the github repo by clicking here.
COMMAND LINE ARGUMENTS¶
Once the environment has been configured, the application can be executed by
./array_partition <matmul XCLBIN>