Developer Guide

FPGA Optimization Guide for Intel® oneAPI Toolkits

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ID 767853
Date 7/13/2023
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Document Table of Contents
Document Table of Contents x
FPGA Optimization Guide for Intel® oneAPI Toolkits Introduction To FPGA Design Concepts Analyze Your Design Optimize Your Design FPGA Optimization Flags, Attributes, Pragmas, and Extensions Quick Reference Additional Information Document Revision History for the FPGA Optimization Guide for Intel® oneAPI Toolkits
Introduction To FPGA Design Concepts x
FPGA Architecture Overview Concepts of FPGA Hardware Design Methods of Hardware Design How Source Code Becomes a Custom Hardware Datapath Scheduling Mapping Parallelism Models to FPGA Hardware Memory Types
FPGA Architecture Overview x
Adaptive Logic Module (ALM) Lookup Table (LUT) Register Digital Signal Processing (DSP) Block Random Access Memory (RAM) Blocks
Concepts of FPGA Hardware Design x
Maximum Frequency (fMAX) Latency Pipelining Throughput Datapath Control Path Occupancy
How Source Code Becomes a Custom Hardware Datapath x
Mapping Source Code Instructions to Hardware Mapping Arrays and Their Accesses to Hardware
Scheduling x
Dynamic Scheduling Clustering the Datapath Handshaking Between Clusters
Mapping Parallelism Models to FPGA Hardware x
Data Parallelism Task Parallelism
Data Parallelism x
Executing Independent Operations Simultaneously Pipelining
Memory Types x
Kernel Memory Global Memory
Analyze Your Design x
Analyze the FPGA Early Image Analyze the FPGA Image
Analyze the FPGA Early Image x
Review the FPGA Optimization Report Access HLD FPGA Reports in JSON Format
Review the FPGA Optimization Report x
Loop Analysis Bottlenecks Viewer Area Estimates System Viewer Kernel Memory Viewer Schedule Viewer
Analyze the FPGA Image x
Quartus (Static) Summary Intel® FPGA Dynamic Profiler for DPC++ System-level Profiling Using the Intercept Layer for OpenCL* Applications
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 Runtime Reduce Area Resource Use While Profiling Profiler Analyses of Example SYCL* Design Scenarios Limitations
Obtain Profiling Data During Runtime x
Invoke the Profiler Runtime Wrapper to Obtain Profiling Data Use Intel® VTune™ Profiler
Use Intel® VTune™ Profiler x
Interpret Performance Counter Data
System-level Profiling Using the Intercept Layer for OpenCL* Applications x
Set Up the Intercept Layer for OpenCL* Applications
Optimize Your Design x
Throughput Resource Use
Throughput x
Single Work-item Kernels NDRange Kernels Memory Accesses Pipes Host
Single Work-item Kernels x
Single Work-item Kernel Design Guidelines Loops Single-Cycle Floating-Point Accumulator for Single Work-Item Kernels
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 Shannonization to Improve FMAX/II Optimize Inner Loop Throughput Improve Loop Performance by Caching On-Chip Memory
Single-Cycle Floating-Point Accumulator for Single Work-Item Kernels x
Strategies for Inferring the Accumulator
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
Pipes x
Host Pipes
Host Pipes x
Host Pipe Declaration Host Pipe API Host Pipes RTL Interfaces
Host x
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
Double Buffering Host Utilizing Kernel Invocation Queue x
Applying Double-Buffering Using the Intercept Layer for OpenCL* Applications
Resource Use x
Data Types and Operations Kernel Variable Accesses
Data Types and Operations x
Optimize Floating-point Operation Avoid Expensive Functions Variable-Precision Integer and Floating-Point Support
Variable-Precision Integer and Floating-Point Support x
Advantages and Limitations of Arbitrary 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 ap_float 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
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) FPGA Extensions
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)
Optimization Targets x
Minimum Latency Flow Maximum Throughput Without Area Optimization Heuristics 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)
FPGA Extensions x
Pipes Extension Asynchronous Parallelism Within Kernels (task_sequence) device_global Extension (Beta)
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 Pipe and Atomic Fence
Asynchronous Parallelism Within Kernels (task_sequence) x
Task Functions Task Invocation Interface Scope, Lifetime, and Reuse of task_sequence Objects Adding Capacity When Launching Task Functions Adding Capacity When Collecting Task Functions task_sequence Use Cases
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
Document Revision History for the FPGA Optimization Guide for Intel® oneAPI Toolkits x
Notices and Disclaimers
FPGA Optimization Guide for Intel® oneAPI Toolkits
Introduction To FPGA Design Concepts
Analyze Your Design
Optimize Your Design
FPGA Optimization Flags, Attributes, Pragmas, and Extensions
Quick Reference
Additional Information
Document Revision History for the FPGA Optimization Guide for Intel® oneAPI Toolkits

Task Functions

Task functions for the task_sequence class are defined as a C++ function. A reference to this C++ function is used as the first template parameter to a specific parameterization of the task_sequence class. More specifically, the template parameter is an auto reference to a callable f that defines the asynchronous task to be associated with the task_sequence class. The requirement for an auto reference amounts to a requirement that f be statically resolvable at compile time. For example, f cannot be a function pointer. Furthermore, the return type and argument types of f must be resolvable and fixed for each parameterization of the task_sequence class.

Task Invocation Interface

You can invoke the task function f with the async() function, which accepts the same arguments as f (both in terms of argument type and order), and stores the return value in a FIFO queue upon completion of f. The async() function call is non-blocking, and it returns before the asynchronous f invocation completes executing and potentially before f even begins executing, as the return type from the async() function provides no implicit information on the execution status of f.

The get() function retrieves the oldest result from this logical FIFO queue and blocks (waits) until a result is available if no result is available immediately upon the call to the get() function. The return type of the get() function is the same as the return type of the task function f.

You can invoke both functions only on the device on which you have declared a task_sequence object. Calling async() or get() function on a different device results in undefined behavior.

In the optimization report (report.html), async() and get() function calls appear as blocking pipe write and pipe read operations.

The following example uses the task function mult to parameterize the task_sequence class with two object declarations, task1 and task2. These objects asynchronously call mult() function twice using the async() function on each object, with their results collected by calls to the get() function on each object. 

int mult(int a, int b) {
	 return a * b;
}
int mult_and_add(int a1, int b1, int a2, int b2) {
  task_sequence<mult> task1, task2;
  task1.async(a1, b1);
  task2.async(a2, b2);
  return task1.get() + task2.get();
}
NOTE:

The arguments to the async() function match the arguments to the mult() function (that is (int, int)). The return type to the get() function matches the return type to the mult() function (that is (int)).

Scope, Lifetime, and Reuse of task_sequence Objects

task_sequence objects must follow these guidelines:

  • Object declarations of a parameterized task_sequence class must be local, which means global declarations and dynamic allocations are not allowed.
  • task_sequence objects must not have their lifetime extended beyond the scope in which they are declared. It is undefined behavior if their lifetime is extended. Both move and copy constructors for the task_sequence class are therefore deleted.
  • Each task_sequence class object represents a specific instantiation of FPGA hardware to perform the task function operation.
  • Launching tasks via the async() function calls on the same object results in the reuse of that object's hardware. Thus, you can control the reuse or replication of FPGA hardware by the number of task_sequence objects you declare. Since object lifetime is confined to the scope in which the task_sequence object is created, carefully declare your object in the scope in which you intend to perform its reuse.
  • A task function associated with a task_sequence can contain task_sequence declarations. In such cases, each object instantiation of the base task_sequence class results in new object declaration of the contained objects. In the following example, baseTask and childTask are task functions that parameterize the task_sequence class. In the kernel code, two task_sequence<baseTask> objects are declared, which means that two hardware instances implementing baseTask are instantiated. Since baseTask includes two declarations of task_sequence<childTask>, each task_sequence<baseTask> object instantiates two task_sequence<childTask> objects. and therefore two hardware instantiations of childTask. Since there are two task_sequence<baseTask> objects in this code, there are a total of four task_sequence<childTask> objects and four hardware instantiations of childTask. 
    void childTask() {
  // useful computation here
  …
}
void baseTask() {
  task_sequence<childTask> child1, child2;
  // useful computation here, including async() and get() on child1, child2
  …
}
// in kernel code
{
  task_sequence<baseTask> base1, base2;
  …
}
  • Before exiting scope, task_sequence objects must retire all outstanding async() function invocations. This is guaranteed by the task_sequence destructor, which calls the get() function several times matching the count of outstanding async() functions calls.

Adding Capacity When Launching Task Functions

Consider specifying the invocation_capacity parameter of the task_sequence class if you observe stall patterns in your simulation waveforms that indicate an imbalance between the following:

  • Any backpressure introduced by the task function.
  • How often you invoke the task function using the async() function.

Adding Capacity When Collecting Task Functions

Consider specifying the response_capacity parameter of the task_sequence class if you observe stall patterns in your design waveforms that indicate a difference in the following:

  • The cadence of data production in the task function.
  • The cadence of collecting task results via the get() function.

Parent topic: Asynchronous Parallelism Within Kernels (task_sequence)
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