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

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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 Cached Write-Acknowledge (write-ack) Nonaligned Never-stall 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

3.6.2. Load-Store Unit Modifiers

Depending on the memory access pattern in your kernel, the compiler modifies some LSUs.

Cached

Burst-coalesced LSUs might sometimes include a cache. A cache is created when the memory access pattern is data-dependent or appears to be repetitive. The cache cannot be shared with other loads even if the loads want the same data. The cache is flushed on kernel start and consumes more hardware resources than an equivalent LSU without a cache. The cache is inferred only for non-volatile global pointers. 

kernel void cached (global int * restrict in, 
                    global int * restrict out, 
                    int N) {
  int gid = get_global_id(0);
  for (int i = 0; i < N; i++) {
    out[N*gid + i] = in[i];
  }
}

Write-Acknowledge (write-ack)

Burst-coalesced store LSUs sometimes require a write-acknowledgment signal when data dependencies exist. LSUs with a write-acknowledge signal require additional hardware resources. Throughput might be reduced if multiple write-acknowledge LSUs access the same memory. 

kernel void write_ack (global int * restrict in, 
                       global int * restrict out, 
                       int N) {
  for (int i = 0; i < N; i++) {
    if (i < 2)
      out[i] = 0;            // Burst-coalesced write-ack LSU
    out[i] = in[i];
  }
}

Nonaligned

When a burst-coalesced LSU can access memory that is not aligned to the external memory word size, a nonaligned LSU is created. Additional hardware resources are required to implement a nonaligned LSU. The throughput of a nonaligned LSU might be reduced if it receives many unaligned requests. 

kernel void non_aligned (global int * restrict in, 
                         global int * restrict out) {
  int i = get_global_id(0);
  
  // Three loads are statically coalesced into one, 
  // creating a burst-coalesced non-aligned LSU.
  int a1 = in[3*i+0];
  int a2 = in[3*i+1];
  int a3 = in[3*i+2];
  
  // Three stores statically coalesced into one, 
  // creating a burst-coalesced non-aligned LSU.
  out[3*i+0] = a3;
  out[3*i+1] = a2;
  out[3*i+2] = a1;
}

Never-stall

If a pipelined LSU is connected to a local memory without arbitration, a never-stall LSU is created because all accesses to the memory take a fixed number of cycles that are known to the compiler. 

__attribute((reqd_work_group_size(1024,1,1)))
kernel void never_stall (global int* restrict in, 
                         global int* restrict out, 
                         int N) {
  local int lmem[1024];
  int gi = get_global_id(0);
  int li = get_local_id(0);

  lmem[li] = in[gi];                   // Pipelined never-stall LSU
  barrier(CLK_GLOBAL_MEM_FENCE);
  out[gi] = lmem[li] ^ lmem[li + 1];
}
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