Intel® FPGA SDK for OpenCL™ Standard Edition: Best Practices Guide

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ID 683176
Date 9/24/2018
Public
Document Table of Contents
Document Table of Contents x
1. Introduction to Standard Edition Best Practices Guide 2. Reviewing Your Kernel's report.html File 3. OpenCL Kernel Design Best Practices 4. Profiling Your Kernel to Identify Performance Bottlenecks 5. Strategies for Improving Single Work-Item Kernel Performance 6. Strategies for Improving NDRange Kernel Data Processing Efficiency 7. Strategies for Improving Memory Access Efficiency 8. Strategies for Optimizing FPGA Area Usage A. Additional Information
1. Introduction to Standard Edition Best Practices Guide x
1.1. FPGA Overview 1.2. Pipelines 1.3. Single Work-Item Kernel versus NDRange Kernel 1.4. Multi-Threaded Host Application
2. Reviewing Your Kernel's report.html File x
2.1. High Level Design Report Layout 2.2. Reviewing the Report Summary 2.3. Reviewing Loop Information 2.4. Reviewing Area Information 2.5. Verifying Information on Memory Replication and Stalls 2.6. Optimizing an OpenCL Design Example Based on Information in the HTML Report 2.7. HTML Report: Area Report Messages 2.8. HTML Report: Kernel Design Concepts
2.3. Reviewing Loop Information x
2.3.1. Loop Analysis Report of an OpenCL Design Example 2.3.2. Changing the Memory Access Pattern Example 2.3.3. Reducing the Area Consumed by Nested Loops Using loop_coalesce
2.4. Reviewing Area Information x
2.4.1. Area Analysis by Source 2.4.2. Area Analysis of System
2.5. Verifying Information on Memory Replication and Stalls x
2.5.1. Features of the System Viewer 2.5.2. Features of the Kernel Memory Viewer
2.7. HTML Report: Area Report Messages x
2.7.1. Area Report Message for Board Interface 2.7.2. Area Report Message for Function Overhead 2.7.3. Area Report Message for State 2.7.4. Area Report Message for Feedback 2.7.5. Area Report Message for Constant Memory 2.7.6. Area Report Messages for Private Variable Storage
2.8. HTML Report: Kernel Design Concepts x
2.8.1. Kernels 2.8.2. Global Memory Interconnect 2.8.3. Local Memory 2.8.4. Nested Loops 2.8.5. Loops in a Single Work-Item Kernel 2.8.6. Channels 2.8.7. Load-Store Units
3. OpenCL Kernel Design Best Practices x
3.1. Transferring Data Via Channels or OpenCL Pipes 3.2. Unrolling Loops 3.3. Optimizing Floating-Point Operations 3.4. Allocating Aligned Memory 3.5. Aligning a Struct with or without Padding 3.6. Maintaining Similar Structures for Vector Type Elements 3.7. Avoiding Pointer Aliasing 3.8. Avoid Expensive Functions 3.9. Avoiding Work-Item ID-Dependent Backward Branching
3.1. Transferring Data Via Channels or OpenCL Pipes x
3.1.1. Characteristics of Channels and Pipes 3.1.2. Execution Order for Channels and Pipes 3.1.3. Optimizing Buffer Inference for Channels or Pipes 3.1.4. Best Practices for Channels and Pipes
3.3. Optimizing Floating-Point Operations x
3.3.1. Floating-Point versus Fixed-Point Representations
4. Profiling Your Kernel to Identify Performance Bottlenecks x
4.1. Best Practices 4.2. GUI 4.3. Interpreting the Profiling Information 4.4. Limitations
4.2. GUI x
4.2.1. Source Code Tab 4.2.2. Kernel Execution Tab 4.2.3. Autorun Captures Tab
4.2.1. Source Code Tab x
4.2.1.1. Tool Tip Options
4.3. Interpreting the Profiling Information x
4.3.1. Stall, Occupancy, Bandwidth 4.3.2. Activity 4.3.3. Cache Hit 4.3.4. Profiler Analyses of Example OpenCL Design Scenarios 4.3.5. Autorun Profiler Data
4.3.1. Stall, Occupancy, Bandwidth x
4.3.1.1. Stalling Channels
4.3.4. Profiler Analyses of Example OpenCL Design Scenarios x
4.3.4.1. High Stall Percentage 4.3.4.2. Low Occupancy Percentage 4.3.4.3. Low Bandwidth Efficiency 4.3.4.4. High Stall and High Occupancy Percentages 4.3.4.5. No Stalls, Low Occupancy Percentage, and Low Bandwidth Efficiency 4.3.4.6. No Stalls, High Occupancy Percentage, and Low Bandwidth Efficiency 4.3.4.7. Stalling Channels 4.3.4.8. High Stall and Low Occupancy Percentages
5. Strategies for Improving Single Work-Item Kernel Performance x
5.1. Addressing Single Work-Item Kernel Dependencies Based on Optimization Report Feedback 5.2. Removing Loop-Carried Dependencies Caused by Accesses to Memory Arrays 5.3. Good Design Practices for Single Work-Item Kernel
5.1. Addressing Single Work-Item Kernel Dependencies Based on Optimization Report Feedback x
5.1.1. Removing Loop-Carried Dependency 5.1.2. Relaxing Loop-Carried Dependency 5.1.3. Simplifying Loop-Carried Dependency 5.1.4. Transferring Loop-Carried Dependency to Local Memory 5.1.5. Removing Loop-Carried Dependency by Inferring Shift Registers
6. Strategies for Improving NDRange Kernel Data Processing Efficiency x
6.1. Specifying a Maximum Work-Group Size or a Required Work-Group Size 6.2. Kernel Vectorization 6.3. Multiple Compute Units 6.4. Combination of Compute Unit Replication and Kernel SIMD Vectorization 6.5. Reviewing Kernel Properties and Loop Unroll Status in the HTML Report
6.2. Kernel Vectorization x
6.2.1. Static Memory Coalescing
6.3. Multiple Compute Units x
6.3.1. Compute Unit Replication versus Kernel SIMD Vectorization
7. Strategies for Improving Memory Access Efficiency x
7.1. General Guidelines on Optimizing Memory Accesses 7.2. Optimize Global Memory Accesses 7.3. Performing Kernel Computations Using Constant, Local or Private Memory 7.4. Improving Kernel Performance by Banking the Local Memory 7.5. Optimizing Accesses to Local Memory by Controlling the Memory Replication Factor 7.6. Minimizing the Memory Dependencies for Loop Pipelining
7.2. Optimize Global Memory Accesses x
7.2.1. Contiguous Memory Accesses 7.2.2. Manual Partitioning of Global Memory
7.2.2. Manual Partitioning of Global Memory x
7.2.2.1. Heterogeneous Memory Buffers
7.3. Performing Kernel Computations Using Constant, Local or Private Memory x
7.3.1. Constant Cache Memory 7.3.2. Preloading Data to Local Memory 7.3.3. Storing Variables and Arrays in Private Memory
7.4. Improving Kernel Performance by Banking the Local Memory x
7.4.1. Optimizing the Geometric Configuration of Local Memory Banks Based on Array Index
8. Strategies for Optimizing FPGA Area Usage x
8.1. Compilation Considerations 8.2. Board Variant Selection Considerations 8.3. Memory Access Considerations 8.4. Arithmetic Operation Considerations 8.5. Data Type Selection Considerations
A. Additional Information x
A.1. Document Revision History for the Standard Edition Best Practices Guide
1. Introduction to Standard Edition Best Practices Guide
2. Reviewing Your Kernel's report.html File
3. OpenCL Kernel Design Best Practices
4. Profiling Your Kernel to Identify Performance Bottlenecks
5. Strategies for Improving Single Work-Item Kernel Performance
6. Strategies for Improving NDRange Kernel Data Processing Efficiency
7. Strategies for Improving Memory Access Efficiency
8. Strategies for Optimizing FPGA Area Usage
A. Additional Information

6.2. Kernel Vectorization

To achieve higher throughput, you can vectorize your kernel. Kernel vectorization allows multiple work-items to execute in a single instruction multiple data (SIMD) fashion. You can direct the to translate each scalar operation in the kernel, such as addition or multiplication, to an SIMD operation.

Include the num_simd_work_items attribute in your kernel code to direct the offline compiler to perform more additions per work-item without modifying the body of the kernel. The following code fragment applies a vectorization factor of four to the original kernel code: 

__attribute__((num_simd_work_items(4)))
__attribute__((reqd_work_group_size(64,1,1)))
__kernel void sum (__global const float * restrict a,
                   __global const float * restrict b,
                   __global float * restrict answer)
{
   size_t gid = get_global_id(0);

   answer[gid] = a[gid] + b[gid];
}

To use the num_simd_work_items attribute, you must also specify a required work-group size of the kernel using the reqd_work_group_size attribute. The work-group size you specify for reqd_work_group_size must be divisible by the value you assign to num_simd_work_items. In the code example above, the kernel has a fixed work-group size of 64 work-items. Within each work-group, the work-items are distributed evenly among the four SIMD vector lanes. After the offline compiler implements the four SIMD vector lanes, each work-item now performs four times more work.

The offline compiler vectorizes the code and might coalesce memory accesses. You do not need to change any kernel code or host code because the offline compiler applies these optimizations automatically.

You can vectorize your kernel code manually, but you must adjust the NDRange in your host application to reflect the amount of vectorization you implement. The following example shows the changes in the code when you duplicate operations in the kernel manually: 

__kernel void sum (__global const float * restrict a,
                   __global const float * restrict b,
                   __global float * restrict answer)
{
   size_t gid = get_global_id(0);

   answer[gid * 4 + 0] = a[gid * 4 + 0] + b[gid * 4 + 0];
   answer[gid * 4 + 1] = a[gid * 4 + 1] + b[gid * 4 + 1];
   answer[gid * 4 + 2] = a[gid * 4 + 2] + b[gid * 4 + 2];
   answer[gid * 4 + 3] = a[gid * 4 + 3] + b[gid * 4 + 3];
}

In this form, the kernel loads four elements from arrays a and b, calculates the sums, and stores the results into the array answer. Because the FPGA pipeline loads and stores data to neighboring locations in memory, you can manually direct the offline compiler to coalesce each group of four load and store operations.

Attention: Each work-item handles four times as much work after you implement the manual optimizations. As a result, the host application must use an NDRange that is four times smaller than in the original example. On the contrary, you do not need to adjust the NDRange size when you exploit the automatic vectorization capabilities of the offline compiler. You can adjust the vector width with minimal code changes by using the num_simd_work_items attribute.

Section Content
Static Memory Coalescing

Level Two Title