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

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ID 683521
Date 3/28/2022
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Document Table of Contents
Document Table of Contents x
1. Introduction to Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide 2. Reviewing Your Kernel's report.html File 3. OpenCL Kernel Design Concepts 4. OpenCL Kernel Design Best Practices 5. Profiling Your Kernel to Identify Performance Bottlenecks 6. Strategies for Improving Single Work-Item Kernel Performance 7. Strategies for Improving NDRange Kernel Data Processing Efficiency 8. Strategies for Improving Memory Access Efficiency 9. Strategies for Optimizing FPGA Area Usage 10. Strategies for Optimizing Intel® Stratix® 10 OpenCL Designs 11. Strategies for Improving Performance in Your Host Application 12. Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide Archives A. Document Revision History for the Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide
1. Introduction to Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide x
1.1. FPGA Overview 1.2. Pipelines 1.3. Single Work-Item Kernel versus NDRange Kernel
2. Reviewing Your Kernel's report.html File x
2.1. High-Level Design Report Layout 2.2. Reviewing the Summary Report 2.3. Viewing Throughput Bottlenecks in the Design 2.4. Using Views 2.5. Analyzing Throughput 2.6. Reviewing Area Information 2.7. Optimizing an OpenCL Design Example Based on Information in the HTML Report 2.8. Accessing HLD FPGA Reports in JSON Format
2.4. Using Views x
2.4.1. Features of the System Viewer 2.4.2. Features of the Kernel Memory Viewer 2.4.3. Features of the Schedule Viewer
2.4.1. Features of the System Viewer x
2.4.1.1. Reviewing System Information 2.4.1.2. Reviewing Global Memory Information 2.4.1.3. Reviewing Block Information 2.4.1.4. Reviewing Cluster Information
2.5. Analyzing Throughput x
2.5.1. Reviewing Loop Information
2.6. Reviewing Area Information x
2.6.1. Area Report Message for Board Interface 2.6.2. Area Report Message for Function Overhead 2.6.3. Area Report Message for State 2.6.4. Area Report Message for Feedback 2.6.5. Area Report Messages for Private Variable Storage
2.6.5. Area Report Messages for Private Variable Storage x
2.6.5.1. Area Report Message for Constant Memory
3. OpenCL Kernel Design Concepts x
3.1. Kernels 3.2. Global Memory Interconnect 3.3. Local Memory 3.4. Loops in a Single Work-Item Kernel 3.5. Channels 3.6. Load-Store Units
3.3. Local Memory x
3.3.1. Changing the Memory Access Pattern Example
3.4. Loops in a Single Work-Item Kernel x
3.4.1. Trade-Off Between Initiation Interval and Maximum Frequency 3.4.2. Loop-Carried Dependencies that Affect the Initiation Interval of a Loop 3.4.3. Nested Loops 3.4.4. Loop Speculation 3.4.5. Loop Fusion 3.4.6. Loop Bottlenecks
3.4.3. Nested Loops x
3.4.3.1. Reducing the Area Consumed by Nested Loops Using loop_coalesce
3.6. Load-Store Units x
3.6.1. Load-Store Unit Types 3.6.2. Load-Store Unit Modifiers 3.6.3. Controlling the Load-Store Units 3.6.4. When to Use Each LSU
4. OpenCL Kernel Design Best Practices x
4.1. Transferring Data Via Intel® FPGA SDK for OpenCL™ Channels or OpenCL Pipes 4.2. Unrolling Loops 4.3. Optimizing Floating-Point Operations 4.4. Allocating Aligned Memory 4.5. Aligning a Struct with or without Padding 4.6. Maintaining Similar Structures for Vector Type Elements 4.7. Avoiding Pointer Aliasing 4.8. Avoid Expensive Functions 4.9. Avoiding Work-Item ID-Dependent Backward Branching
4.1. Transferring Data Via Intel® FPGA SDK for OpenCL™ Channels or OpenCL Pipes x
4.1.1. Characteristics of Channels and Pipes 4.1.2. Execution Order for Channels and Pipes 4.1.3. Optimizing Buffer Inference for Channels or Pipes 4.1.4. Best Practices for Channels and Pipes
4.3. Optimizing Floating-Point Operations x
4.3.1. Floating-Point versus Fixed-Point Representations
5. Profiling Your Kernel to Identify Performance Bottlenecks x
5.1. Best Practices for Profiling Your Kernel 5.2. Instrumenting the Kernel Pipeline with Performance Counters (-profile) 5.3. Obtaining Profiling Data During Runtime 5.4. Reducing Area Resource Use While Profiling 5.5. Temporal Performance Collection 5.6. Performance Data Types 5.7. Interpreting the Profiling Information 5.8. Profiler Analyses of Example OpenCL Design Scenarios 5.9. Intel® FPGA Dynamic Profiler for OpenCL™ Limitations
5.3. Obtaining Profiling Data During Runtime x
5.3.1. Invoking the Profiler Runtime Wrapper 5.3.2. Viewing Profiling Data Using Intel® VTune™ Profiler
5.3.1. Invoking the Profiler Runtime Wrapper x
5.3.1.1. Splitting Execution and Data Post Processing
5.5. Temporal Performance Collection x
5.5.1. Profiling Autorun Kernels
5.7. Interpreting the Profiling Information x
5.7.1. Stall, Occupancy, Bandwidth 5.7.2. Stalling Channels 5.7.3. Channel Depths
5.8. Profiler Analyses of Example OpenCL Design Scenarios x
5.8.1. High Stall Percentage 5.8.2. Low Occupancy Percentage 5.8.3. High Stall and High Occupancy Percentages 5.8.4. No Stalls, Low Occupancy Percentage, and Low Bandwidth 5.8.5. No Stalls, High Occupancy Percentage, and Low Bandwidth 5.8.6. High Stall and Low Occupancy Percentages
6. Strategies for Improving Single Work-Item Kernel Performance x
6.1. Addressing Single Work-Item Kernel Dependencies Based on Optimization Report Feedback 6.2. Good Design Practices for Single Work-Item Kernel
6.1. Addressing Single Work-Item Kernel Dependencies Based on Optimization Report Feedback x
6.1.1. Removing Loop-Carried Dependency 6.1.2. Relaxing Loop-Carried Dependency 6.1.3. Transferring Loop-Carried Dependency to Local Memory 6.1.4. Relaxing Loop-Carried Dependency by Inferring Shift Registers 6.1.5. Removing Loop-Carried Dependencies Caused by Accesses to Memory Arrays
7. Strategies for Improving NDRange Kernel Data Processing Efficiency x
7.1. Specifying a Maximum Work-group Size or a Required Work-Group Size 7.2. Kernel Vectorization 7.3. Multiple Compute Units 7.4. Combination of Compute Unit Replication and Kernel SIMD Vectorization 7.5. Reviewing Kernel Properties and Loop Unroll Status in the HTML Report
7.3. Multiple Compute Units x
7.3.1. Compute Unit Replication versus Kernel SIMD Vectorization
8. Strategies for Improving Memory Access Efficiency x
8.1. General Guidelines on Optimizing Memory Accesses 8.2. Optimize Global Memory Accesses 8.3. Performing Kernel Computations Using Constant, Local or Private Memory 8.4. Improving Kernel Performance by Banking the Local Memory 8.5. Optimizing Accesses to Local Memory by Controlling the Memory Replication Factor 8.6. Minimizing the Memory Dependencies for Loop Pipelining 8.7. Static Memory Coalescing
8.2. Optimize Global Memory Accesses x
8.2.1. Contiguous Memory Accesses 8.2.2. Manual Partitioning of Global Memory 8.2.3. Optimizing for Two or More Banks of Global Memory
8.2.2. Manual Partitioning of Global Memory x
8.2.2.1. Heterogeneous Memory Buffers
8.3. Performing Kernel Computations Using Constant, Local or Private Memory x
8.3.1. Constant Cache Memory 8.3.2. Preloading Data to Local Memory 8.3.3. Storing Variables and Arrays in Private Memory
8.4. Improving Kernel Performance by Banking the Local Memory x
8.4.1. Optimizing the Geometric Configuration of Local Memory Banks Based on Array Index
9. Strategies for Optimizing FPGA Area Usage x
9.1. Compilation Considerations 9.2. Board Variant Selection Considerations 9.3. Memory Access Considerations 9.4. Arithmetic Operation Considerations 9.5. Data Type Selection Considerations
10. Strategies for Optimizing Intel® Stratix® 10 OpenCL Designs x
10.1. Reducing Channel Overhead 10.2. Optimizing Loop Control 10.3. Simplifying Memory Access to Local Memories 10.4. On-Chip Storage of Reused Data 10.5. Optimizing Data Path Control 10.6. Creating RTL Modules
10.1. Reducing Channel Overhead x
10.1.1. Reducing the Number of Kernels 10.1.2. Using a Single Kernel to Describe Systolic Arrays 10.1.3. Using Non-Blocking Channels
10.2. Optimizing Loop Control x
10.2.1. Simplifying Loop-Carried Dependencies in Intel® Stratix® 10 OpenCL Designs
10.6. Creating RTL Modules x
10.6.1. Reset Recommendations
11. Strategies for Improving Performance in Your Host Application x
11.1. Multi-Threaded Host Application 11.2. Utilizing Hardware Kernel Invocation Queue
11.2. Utilizing Hardware Kernel Invocation Queue x
11.2.1. Double Buffered Host Application Utilizing Kernel Invocation Queue
1. Introduction to Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide
2. Reviewing Your Kernel's report.html File
3. OpenCL Kernel Design Concepts
4. OpenCL Kernel Design Best Practices
5. Profiling Your Kernel to Identify Performance Bottlenecks
6. Strategies for Improving Single Work-Item Kernel Performance
7. Strategies for Improving NDRange Kernel Data Processing Efficiency
8. Strategies for Improving Memory Access Efficiency
9. Strategies for Optimizing FPGA Area Usage
10. Strategies for Optimizing Intel® Stratix® 10 OpenCL Designs
11. Strategies for Improving Performance in Your Host Application
12. Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide Archives
A. Document Revision History for the Intel® FPGA SDK for OpenCL™ Pro Edition Best Practices Guide

8. Strategies for Improving Memory Access Efficiency

Memory access efficiency often dictates the overall performance of your OpenCL™ kernel. When developing your OpenCL code, it is advantageous to minimize the number of global memory accesses. The OpenCL Specification version 1.0 describes four memory types: global, constant, local, and private memories.
Tip: For Intel® oneAPI DPC++/C++ Compiler-specific details, refer to Memory Accesses section in the Intel® oneAPI DPC++ FPGA Optimization Guide.

An interconnect topology connects shared global, constant, and local memory systems to their underlying memory. Interconnect includes access arbitration to memory ports.

Memory accesses compete for shared memory resources (that is, global, local, and constant memories). If your OpenCL kernel performs a large number of memory accesses, the Intel® FPGA SDK for OpenCL™ Offline Compiler must generate complex arbitration logic to handle the memory access requests. The complex arbitration logic might cause a drop in the maximum operating frequency (fMAX), which degrades kernel performance.

The following sections discuss memory access optimizations in detail. In summary, minimizing global memory accesses is beneficial for the following reasons:

  • Typically, increases in OpenCL kernel performance lead to increases in global memory bandwidth requirements.
  • The maximum global memory bandwidth is much smaller than the maximum local memory bandwidth.
  • The maximum computational bandwidth of the FPGA is much larger than the global memory bandwidth.
    Attention: Use local, private or constant memory whenever possible to increase the memory bandwidth of the kernel.
  1. General Guidelines on Optimizing Memory Accesses
    Optimizing the memory accesses in your OpenCL™ kernels can improve overall kernel performance.
  2. Optimize Global Memory Accesses
    The offline compiler interleaves global memory across each of the external memory banks.
  3. Performing Kernel Computations Using Constant, Local or Private Memory
    To optimize memory access efficiency, minimize the number for global memory accesses by performing your OpenCL™ kernel computations in constant, local, or private memory.
  4. Improving Kernel Performance by Banking the Local Memory
    Specifying the numbanks(N) and bankwidth(M) advanced kernel attributes allows you to configure the local memory banks for parallel memory accesses.
  5. Optimizing Accesses to Local Memory by Controlling the Memory Replication Factor
    To control the memory replication factor, use the max_replicates kernel attribute in your OpenCL™ kernel.
  6. Minimizing the Memory Dependencies for Loop Pipelining
    Intel® FPGA SDK for OpenCL™ Offline Compiler ensures that the memory accesses from the same thread respects the program order. When you compile an NDRange kernel, use barriers to synchronize memory accesses across threads in the same work-group.
  7. Static Memory Coalescing
    Static memory coalescing is an Intel® FPGA SDK for OpenCL™ Offline Compiler optimization step that attempts to reduce the number of times a kernel accesses non-private memory.
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