Developer Guide

Intel oneAPI DPC++/C++ Compiler Handbook for Intel FPGAs

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ID 785441
Date 5/08/2024
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Document Table of Contents
Document Table of Contents x
Intel oneAPI DPC++/C++ Compiler Handbook for Intel FPGAs Introduction To FPGA Design Concepts Intel oneAPI FPGA Development Getting Started with the Intel oneAPI DPC++/C++ Compiler for Intel FPGA Development Defining a Kernel for FPGAs Debugging and Verifying Your Design Analyzing Your Design Optimizing Your Kernel Optimizing Your Host Application Integrating Your Kernel into DSP Builder for Intel FPGAs Integrating Your RTL IP Core Into a System RTL IP Core Kernel Interfaces Loops Pipes Data Types and Arithmetic Operations Parallelism Memories and Memory Operations Libraries Additional FPGA Acceleration Flow Considerations Additional SYCL* HLS Flow Considerations FPGA Optimization Flags, Attributes, Pragmas, and Extensions Quick Reference Additional Information Document Revision History for the Intel oneAPI DPC++/C++ Compiler Handbook for Intel FPGAs Notices and Disclaimers
Introduction To FPGA Design Concepts x
FPGA Architecture Overview Concepts of FPGA Hardware Design How Source Code Becomes a Custom Hardware Datapath
How Source Code Becomes a Custom Hardware Datapath x
Mapping Source Code Instructions to Hardware Mapping Operations to Hardware Mapping Arrays and Their Accesses to Hardware Scheduling Mapping Parallelism Models to FPGA Hardware Memory Types
Intel oneAPI FPGA Development x
FPGA Flow Terminology Intel oneAPI FPGA Development Flow Types of SYCL* FPGA Compilation FPGA Compilation Flags FPGA Workflows in IDEs
Types of SYCL* FPGA Compilation x
Separating Device and Host Code Compilation
Getting Started with the Intel oneAPI DPC++/C++ Compiler for Intel FPGA Development x
Installing the Intel oneAPI FPGA Development Environment FPGA Development for Intel oneAPI Toolkits with Visual Studio* Code
Installing the Intel oneAPI FPGA Development Environment x
Simulation Prerequisites Installing the Questa*-Intel FPGA Edition Software Set Up the Simulation Environment
FPGA Development for Intel oneAPI Toolkits with Visual Studio* Code x
Set the Environment Variables and Launch Visual Studio* Code Create an FPGA Visual Studio* Code Project Enable Code Completion in a Visual Studio* Code Project Configure Running and Debugging in a Visual Studio* Code Project Debugging Your Kernel in Visual Studio* Code with a Native Debugger Generate and View the FPGA Optimization Report Build and Run the FPGA Hardware Image
Defining a Kernel for FPGAs x
Suggested Kernel Coding Styles Single Work-Item Kernels
Single Work-Item Kernels x
Single Work-item Kernel Design Guidelines Single-Cycle Floating-Point Accumulator for Single Work-Item Kernels
Single-Cycle Floating-Point Accumulator for Single Work-Item Kernels x
Strategies for Inferring the Accumulator
Debugging and Verifying Your Design x
Device Selectors for FPGA printf Command Restrictions Emulate and Debug Your Design Evaluate Your Kernel Through Simulation
Emulate and Debug Your Design x
Emulator Environment Variables Compile and Emulate Your Design Limitations of the Emulator Troubleshooting Discrepancies in Hardware and Emulator Results Emulator Known Issues
Evaluate Your Kernel Through Simulation x
Debug During Verification Compile a Kernel for Simulation Simulate Your Kernel Viewing Simulation Waveforms Troubleshooting Simulator Issues
Analyzing Your Design x
Review the FPGA Optimization Report Quartus (Static) Summary Intel® FPGA Dynamic Profiler for DPC++
Review the FPGA Optimization Report x
Loop Analysis Bottlenecks Viewer Area Estimates System Viewer Kernel Memory Viewer Schedule Viewer Access FPGA Optimization Reports in JSON Format
Quartus (Static) Summary x
Timing Failures
Intel® FPGA Dynamic Profiler for DPC++ x
Measure Kernel Performance Instrument the Kernel Pipeline with Performance Counters (-Xsprofile) Obtain Profiling Data During Run Time Reduce Area Resource Use While Profiling Profiler Analyses of Example SYCL* Design Scenarios Limitations
Obtain Profiling Data During Run Time x
Invoke the Profiler Runtime Wrapper to Obtain Profiling Data Use Intel® VTune™ Profiler
Use Intel® VTune™ Profiler x
Interpret Performance Counter Data
Optimizing Your Host Application x
Throughput Resource Use System-level Profiling Using the Intercept Layer for OpenCL™ Applications Multi-Threaded Host Application Utilizing Hardware Kernel Invocation Queue Double Buffering Host Utilizing Kernel Invocation Queue N-Way Buffering to Overlap Kernel Execution Prepinning Memory Simple Host-Device Streaming Buffered Host-Device Streaming
System-level Profiling Using the Intercept Layer for OpenCL™ Applications x
Set Up the Intercept Layer for OpenCL™ Applications
Double Buffering Host Utilizing Kernel Invocation Queue x
Analyzing Buffering Using the Intercept Layer for OpenCL™ Applications
Integrating Your RTL IP Core Into a System x
Synthesize Your RTL IP Core with Quartus Prime Software Encrypting RTL IP Cores Add an RTL IP Core into a Platform Designer System Add an RTL IP Core into a Quartus Prime Project
Encrypting RTL IP Cores x
Encrypting RTL IP Cores Without Licensing Encrypting RTL IP Core With Licensing
RTL IP Core Kernel Interfaces x
Kernel Argument Interfaces Memory-Mapped (MM) Agent Kernel Invocation Interface Ready/Valid Handshaking Kernel Invocation Interface Memory-Mapped Host Interfaces
Memory-Mapped Host Interfaces x
Memory-Mapped Host Interfaces Using Unified Shared Memory Pointers and the annotated_arg Class Memory-Mapped Host Interfaces Using Accessors
Memory-Mapped Host Interfaces Using Unified Shared Memory Pointers and the annotated_arg Class x
Buffer Locations in Memory-Mapped Host Interfaces The annotated_arg Template Class
Loops x
Refactor the Loop-Carried Data Dependency Relax Loop-Carried Dependency Transfer Loop-Carried Dependency to Local Memory Minimize the Memory Dependencies for Loop Pipelining Unroll Loops Fuse Loops to Reduce Overhead and Improve Performance Optimize Loops With Loop Speculation Remove Loop Bottlenecks Improve fMAX/II with Shannonization Optimize Inner Loop Throughput Improve Loop Performance by Caching Data in On-Chip Memory
Pipes x
Host Pipes Pipes Extension Emulate Applications with a Pipe That Reads or Writes to an I/O Pipe
Host Pipes x
Host Pipe Declaration Host Pipe API Host Pipes RTL Interfaces
Pipes Extension x
Key Properties of a Pipe Accessing Pipes The pipe Class and its Use I/O Pipes Characteristics of Pipes Restrictions of Pipes Guidelines for Designing Pipes The atomic_fence Function and Pipes
Data Types and Arithmetic Operations x
Optimize Floating-point Operation Avoid Expensive Functions Variable-Precision Data Type Support
Variable-Precision Data Type Support x
Advantages and Limitations of Variable Precision Data Types Declare and Use the AC Data Types
Declare and Use the AC Data Types x
Declare the ac_int Data Type Declare the ac_fixed Data Type Declare the ac_complex Data Type Declare the ap_float Data Type
Declare the ap_float Data Type x
Conversion Rules for the ap_float Data Type Operations with Explicit Precision Controls Comparison Operators Additional ap_float Functions Additional Data Types Provided by the ap_float.hpp Header File Quality of Results and the ap_float Data Type
Parallelism x
Pipelined Kernels NDRange Kernels Asynchronous Parallelism Within Kernels (task_sequence)
Pipelined Kernels x
Stable Arguments
Asynchronous Parallelism Within Kernels (task_sequence) x
Task Functions task_sequence Use Cases
Memories and Memory Operations x
The device_global Extension (Beta) The annotated_ptr Template Class (Beta) Memory Accesses Kernel Variable Accesses
Memory Accesses x
Load-Store Units Global Memory Accesses Optimization Perform Kernel Computations Using Local or Private Memory Local and Private Memory Accesses Optimization Annotating Unified Shared Memory Pointers Zero-Copy Memory Access Additional Recommendations
Load-Store Units x
Load-Store Unit Styles Load-Store Unit Modifiers Load-Store Unit Controls
Global Memory Accesses Optimization x
Global Memory Bandwidth Use Calculation Manual Partition of Global Memory Partitioning Buffers Across Different Memory Types (Heterogeneous Memory) Partitioning Buffers Across Memory Channels of the Same Memory Type Ignoring Dependencies Between Accessor Arguments Contiguous Memory Accesses Static Memory Coalescing
Libraries x
Use SYCL Shared Library With Third-Party Applications Use of RTL Libraries for FPGA Object Manifest File Syntax of an RTL Library Restrictions and Limitations in RTL Support Agilex 7 and Stratix 10 Design-Specific Reset Requirements for Stall-Free and Stallable RTL Libraries Stall-Free RTL
Additional FPGA Acceleration Flow Considerations x
FPGA-CPU Interaction FPGA BSPs and Boards Targeting Multiple Homogeneous FPGA Devices Targeting Multiple Platforms Split Kernel into Multiple FPGA Images (Linux only)
FPGA BSPs and Boards x
FPGA Board Initialization Obtain FPGA Hardware Image Information Extracting the FPGA Hardware Configuration (.aocx) File from a Multiarchitecture Binary File
Additional SYCL* HLS Flow Considerations x
IP core Reset Behavior
FPGA Optimization Flags, Attributes, Pragmas, and Extensions x
Optimization Flags Optimization Targets Kernel Variables Kernel Attributes Memory Attributes Loop Directives Floating-Point Pragmas Latency Controls (Beta)
Optimization Flags x
Specify Schedule fMAX Target for Kernels (-Xsclock=<clock target>) Create a 2xclock Interface (-Xsuse-2xclock) Disable Burst-Interleaving of Global Memory (-Xsno-interleaving=<global_memory_name>) Force Ring Interconnect for Global Memory (-Xsglobal-ring) Force a Single Store Ring to Reduce Area (-Xsforce-single-store-ring) Force Fewer Read Data Reorder Units to Reduce Area (-Xsnum-reorder) Disable Hardware Kernel Invocation Queue (-Xsno-hardware-kernel-invocation-queue) Modify the Handshaking Protocol Between Clusters (-Xshyper-optimized-handshaking) Disable Automatic Fusion of Loops (-Xsdisable-auto-loop-fusion) Fuse Adjacent Loops With Unequal Trip Counts (-Xsenable-unequal-tc-fusion) Pipeline Loops in Non-task Kernels (-Xsauto-pipeline) Control Semantics of Floating-Point Operations (-fp-model=<value>) Modify the Rounding Mode of Floating-point Operations (-Xsrounding=<rounding_type>) Global Control of Exit FIFO Latency of Stall-free Clusters (-Xssfc-exit-fifo-type=<value>) Enable the Read-Only Cache for Read-Only Accessors (-Xsread-only-cache-size=<N>) Control Hardware Implementation of the Supported Data Types and Math Operations (-Xsdsp-mode=<option>) Generate Register Map Wrapper (-Xsregister-map-wrapper-type) Allow Wide Memory Initialization (-Xsallow-wide-mif)
Optimization Targets x
Minimum Latency Flow Minimum Area Flow Balanced Throughput-Area Trade-Offs Flow
Kernel Attributes x
Specify Schedule FMAX Target for Kernels Specify a Workgroup Size Specify Number of SIMD Work Items Omit Hardware that Generates and Dispatches Kernel IDs Omit Hardware to Support the no_global_work_offset Attribute in parallel_for Kernels Reduce Kernel Area and Latency
Loop Directives x
disable_loop_pipelining Attribute initiation_interval Attribute ivdep Attribute loop_coalesce Attribute max_concurrency Attribute max_interleaving Attribute speculated_iterations Attribute unroll Pragma Loop Fuse Functions and nofusion Attribute max_reinvocation_delay Attribute (Beta)
Quick Reference x
Algorithmic C Data Types Floating Point Pragmas FPGA Accessor Properties FPGA Extensions FPGA Kernel Attributes FPGA Local Memory Function Latency Control Properties (Beta) FPGA LSU Controls FPGA Loop Directives FPGA Memory Attributes FPGA Optimization Flags Pipe API
Intel oneAPI DPC++/C++ Compiler Handbook for Intel FPGAs
Introduction To FPGA Design Concepts
Intel oneAPI FPGA Development
Getting Started with the Intel oneAPI DPC++/C++ Compiler for Intel FPGA Development
Defining a Kernel for FPGAs
Debugging and Verifying Your Design
Analyzing Your Design
Optimizing Your Kernel
Optimizing Your Host Application
Integrating Your Kernel into DSP Builder for Intel FPGAs
Integrating Your RTL IP Core Into a System
RTL IP Core Kernel Interfaces
Loops
Pipes
Data Types and Arithmetic Operations
Parallelism
Memories and Memory Operations
Libraries
Additional FPGA Acceleration Flow Considerations
Additional SYCL* HLS Flow Considerations
FPGA Optimization Flags, Attributes, Pragmas, and Extensions
Quick Reference
Additional Information
Document Revision History for the Intel oneAPI DPC++/C++ Compiler Handbook for Intel FPGAs
Notices and Disclaimers

Mapping Source Code Instructions to Hardware

Based on your source code and the following principles, the Intel® oneAPI DPC++/C++ Compiler builds a custom hardware datapath in the FPGA’s logic:

  • The datapath is functionally equivalent to the C++ program described by the source. Once the datapath is constructed, the compiler orchestrates work items and loop iterations such that the hardware is effectively occupied.
  • The compiler builds the custom hardware datapath while minimizing the area of the FPGA resources (ALMs, DSPs, and so on) used by the design.

Mapping Operations to Hardware

For fixed architectures such as CPUs and GPUs, the source code is compiled by the compiler into a set of instructions that run on functional units with a fixed functionality. For these fixed architectures to be useful in a broad range of applications, some of their available functional units are not useful to every program. Unused functional units mean that your program does not fully occupy the fixed architecture hardware.

FPGAs are not subject to these restrictions of fixed functional units. On an FPGA, you can synthesize a specialized hardware datapath that can be fully occupied for an arbitrary set of instructions, which means you can be more efficient with the chip's silicon area.

By implementing your algorithm in hardware, you can fill your chip with custom hardware that is always (or almost always) working on your problem instead of having idle functional units.

The Intel® oneAPI DPC++/C++ Compiler maps statements from the source code to individual specialized hardware operations, as shown in the example in the following image:

Mapping Source Code Instructions to Hardware

In general, each instruction maps to its own unique instance of a hardware operation. However, a single statement may map to more than one hardware operation, or multiple statements may combine into a single hardware operation when the compiler finds that it can generate more efficient hardware.

The latency of hardware operations is dependent on the complexity of the operation and the target fMAX.

The compiler then takes these hardware operations and connects them into a graph based on their dependencies. When operations are independent, the compiler automatically infers parallelism by executing those operations simultaneously in time.

The following figure illustrates a dependency graph created for the hardware datapath:

Dependency Graph

The dependency graph illustrates how the instruction is mapped to hardware operations and how the hardware operations are connected based on their dependencies. The loads in this example instruction are independent of each other and can therefore run simultaneously.

Mapping Arrays and Their Accesses to Hardware

Similar to mapping statements to specialized hardware operations, the compiler maps arrays to hardware memories based on memory access patterns and variable sizes. The datapath interacts with this memory through load/store units (LSUs), which are inferred from array accesses in the source code.

The following figure illustrates a simple example of mapping arrays and their accesses to hardware:

Mapping Arrays and Their Accesses to Hardware

A RAM can have a limited number of read ports and write ports, but a datapath can have many LSUs. When the number of LSUs does not match the available number of read and write ports, the compiler uses techniques like replication, double-pumping, sharing, and arbitration. For more information, refer to Kernel Memory.

NOTE:

FPGAs provide specialized hardware block RAMs that you can configure and combine to match the size of your arrays. Doing so can provide many terabytes per second of on-chip memory bandwidth because each of these memories can interact with the datapath simultaneously.

Arrays might also be implemented in your kernel datapath. In this case, the array contents are stored as registers in the datapath when your algorithm is pipelined (as discussed in Pipelining). Storing array contents as registers in the datapath can improve performance in some cases, but it is a design decision whether to implement an array as registers or as memories.

When you access an array that is implemented as registers, LSUs are not used. The compiler might choose to use a select or a barrel shifter instead.

Memory Access via Select
Parent topic: How Source Code Becomes a Custom Hardware Datapath
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