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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
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
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
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
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
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7. Strategies for Improving NDRange Kernel Data Processing Efficiency
Consider the following kernel code:
__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];
}
This kernel adds arrays a and b, one element at a time. Each work-item is responsible for adding two elements, one from each array, and storing the sum into the array answer. Without optimization, the kernel performs one addition per work-item.
To maximize the performance of your OpenCL™ kernel, consider implementing the applicable optimization techniques to improve data processing efficiency.
- Specifying a Maximum Work-group Size or a Required Work-Group Size
Specify the max_work_group_size or reqd_work_group_size attribute for your kernels whenever possible. These attributes allow the Intel® FPGA SDK for OpenCL™ Offline Compiler to perform aggressive optimizations to match the kernel to hardware resources without any excess logic. - Kernel Vectorization
Kernel vectorization allows multiple work-items to execute in a single instruction multiple data (SIMD) fashion. - Multiple Compute Units
To achieve higher throughput, the Intel® FPGA SDK for OpenCL™ Offline Compiler can generate multiple compute units for each kernel. - Combination of Compute Unit Replication and Kernel SIMD Vectorization
If your replicated or vectorized OpenCL kernel does not fit in the FPGA, you can modify the kernel by both replicating the compute unit and vectorizing the kernel. - Reviewing Kernel Properties and Loop Unroll Status in the HTML Report
When you compile an NDRange kernel, the Intel® FPGA SDK for OpenCL™ Offline Compiler generates a <your_kernel_filename>/reports/report.html file that provides information on select kernel properties and loop unroll status.