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

3.5. Aligning a Struct with or without Padding

A properly aligned struct helps the generate the most efficient hardware.
A proper struct alignment means that the alignment can be evenly divided by the struct size.
Important: Ensure a 4-byte alignment for the data structures. struct alignments smaller than four bytes result in larger and slower hardware. Hardware efficiency increases with the increasing alignment. In the following example, the Pixel_s structure is only one-byte aligned but the Pixel structure is four-byte aligned due to the presence of a four-byte not_used integer: 
typedef struct {
	char r,g,b,alpha;
} Pixel_s;

typedef union {
     Pixel_s p;
     int not_used;
} Pixel;
You can also use the aligned attribute to force a 4-byte alignment, as shown in the following example code: 
typedef struct {
       char r,g,b,alpha;
} __attribute__((aligned(4))) Pixel;

The offline compiler conforms with the ISO C standard that requires the alignment of a struct to satisfy all of the following criteria:

  • The alignment must be an integer multiple of the lowest common multiple between the alignments of all struct members.
  • The alignment must be a power of two.

You may set the struct alignment by including the aligned(N) attribute in your kernel code. Without an aligned attribute, the offline compiler determines the alignment of each struct in an array of struct based on the size of the struct. Consider the following example: 

__kernel void test (struct mystruct* A,
                    struct mystruct* B)
{
    A[get_global_id(0)] = B[get_global_id(0)];
}

If the size of mystruct is 101 bytes, each load or store access will be 1-byte aligned. If the size of mystruct is 128 bytes, each load or store access will be 128-byte aligned, which generates the most efficient hardware.

When the struct fields are not aligned within the struct, the offline compiler inserts padding to align them. Inserting padding between struct fields affects hardware efficiency in the following manner:

  • Increases the size of the struct
  • Might affect the alignment

To prevent the offline compiler from inserting padding, include the packed attribute in your kernel code. The aforementioned ISO C standard applies when determining the alignment of a packed or unpacked struct. Consider the following example: 

struct mystruct1
{
    char a;
    int b;
};

The size of mystruct1 is 8 bytes. Therefore, the struct is 8-byte aligned, resulting in efficient accesses in the kernel. Now consider another example: 

struct mystruct2
{
    char a;
    int b;
    int c;
};

The size of mystruct2 is 12 bytes and the struct is 4-byte aligned. Because the struct fields are padded and the struct is unaligned, accesses in the kernel are inefficient.

Following is an example of a struct that includes the packed attribute: 

struct __attribute__((packed)) mystruct3
{
    char a;
    int b;
    int c;
};

The size of mystruct4 is 16 bytes. Because mystruct4 is aligned and there is no padding between struct fields, accesses in this kernel are more efficient than accesses in mystruct3.

To include both the aligned(N) and packed attributes in a struct, consider the following example: 

struct __attribute__((packed)) __attribute__((aligned(16))) mystruct5
{
    char a;
    int b;
    int c;
};

The size of mystruct5 is 9 bytes. Because of the aligned(16) attribute, the struct is stored at 16-byte aligned addresses in an array. Because mystruct5 is 16-byte aligned and has no padding, accesses in this kernel will be efficient.

For more information on struct alignment and the aligned(N) and packed attributes, refer to the following documents:

  • Section 6.11.1 of the OpenCL Specification version 1.2
  • Disabling Insertion of Data Structure Padding section of the Standard Edition Programming Guide
  • Specifying the Alignment of a Struct section of the Standard Edition Programming Guide
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