FPGA Optimization Guide for Intel® oneAPI Toolkits

Developer Guide

Contents

Local and Private Memory Accesses Optimization

To optimize local memory access efficiency, consider the following guidelines:
  • Implementing certain optimization techniques, such as loop unrolling, might lead to more concurrent memory accesses.
    • Increasing the number of memory accesses can complicate memory systems and degrade performance.
  • If you have function scope local data (defined in the body of a
    parallel_for_work_group lambda
     or by using
    group_local_memory_for_overwrite
    ), the
    Intel® oneAPI
    DPC++/C++
     Compiler
     statically sizes the local data that you define within a function body at compilation time.
  • For accessors in the local space, the host assigns their memory sizes dynamically at runtime. However, the compiler must set these physical memory sizes at compilation time. By default, that size is 16 kB.
    • The host can request a smaller data size than has been physically allocated at compilation time, but never a larger size.
  • When accessing local memory, use the simplest address calculations possible and avoid pointer math operations that are not mandatory.
    • Intel® recommends this coding style to reduce FPGA resource utilization and increase local memory efficiency by allowing the
      Intel® oneAPI
      DPC++/C++
       Compiler
       to make better inferences about access patterns through static code analysis. For information about the benefits of static coalescing, refer to Static Memory Coalescing. Complex address calculations and pointer math operations can prevent the
      Intel® oneAPI
      DPC++/C++
       Compiler
       from identifying the memory system accessed by a given load or store, leading to increased area use and decreased runtime performance.
  • Avoid storing pointers to memory whenever possible. Stored pointers often prevent static compiler analysis from determining the data sets accessed, when pointers are subsequently retrieved from memory. Storing pointers to memory usually leads to suboptimal area and performance results.
  • Create local array elements that are a power of two bytes to allow the
    Intel® oneAPI
    DPC++/C++
     Compiler
     to provide an efficient memory configuration.
    • Whenever possible, the
      Intel® oneAPI
      DPC++/C++
       Compiler
       automatically pads the elements of the local memory to be a power of two to provide a more efficient memory configuration. For example, if you have a
      struct
       containing three chars, the
      Intel® oneAPI
      DPC++/C++
       Compiler
       pads it to four bytes, instead of creating a narrower and deeper memory with multiple accesses (that is, a 1-byte wide memory configuration). However, there are cases where the
      Intel® oneAPI
      DPC++/C++
       Compiler
       might not pad the memory, such as when the kernel accesses local memory indirectly through pointer arithmetic.
    • To determine if the
      Intel® oneAPI
      DPC++/C++
       Compiler
       has padded the local memory, review the memory dimensions in the Memory Viewer. If the
      Intel® oneAPI
      DPC++/C++
       Compiler
       fails to pad the local memory, it prints a warning message in the Area Analysis of System report.

Local Memory Banks

Specifying the 
numbanks(N)
 and 
bankwidth(M)
 memory attributes allow you to configure the local memory banks for parallel memory accesses. The banking geometry described by these attributes determines which elements of the local memory system your kernel can access in parallel.
The following code example depicts an 8 x 4 local memory system that is implemented in a single bank. As a result, no two elements in the system can be accessed in parallel. 
[[intel::fpga_memory]] int lmem[8][4];
#pragma unroll
for(int i = 0; i<4; i+=2) {
  lmem[i][x] = ...; 
}
Serial Accesses to an 8 x 4 Local Memory System
Serial accesses to an 8 x 4 local memory system
To improve performance, you can add 
numbanks(N)
 and 
bankwidth(M)
 in your code to define the number of memory banks and the bank widths in bytes. The following code implements eight memory banks, each 16-bytes wide. This memory bank configuration enables parallel memory accesses to the 8 x 4 array. 
[[intel::numbanks(8), intel::bankwidth(16)]] int lmem[8][4];
#pragma unroll
for (int i = 0; i < 4; i+=2) {
  lmem[i][x & 0x3] = ...; 
}
To enable parallel access, you must mask the dynamic access on the lower array index. Masking the dynamic access on the lower array index informs the 
Intel® oneAPI
DPC++/C++
 Compiler
 that 
x
 does not exceed the lower index bounds.
Parallel Access to an 8 x 4 Local Memory System with Eight 16-Byte-Wide Memory Banks
Parallel access to an 8 x 4 local memory system with eight 16-byte-wide memory banks
By specifying different values for the 
numbanks(N)
 and 
bankwidth(M)
 attributes, you can change the parallel access pattern. The following code implements four memory banks, each being four bytes wide:
This memory bank configuration enables parallel memory accesses to the 8 x 4 array. 
[[intel::numbanks(4), intel::bankwidth(4)]] int lmem[8][4];
#pragma unroll
for (int i = 0; i < 4; i+=2) {
  lmem[x][i] = ...; 
}
Parallel Access to an 8 x 4 Local Memory System with Four 4-Byte-Wide Memory Banks
Parallel access to an 8 x 4 local memory system with four 4-byte-wide memory banks

Optimize the Geometric Configuration of Local Memory Banks Based on Array Index

By default, the 
Intel® oneAPI
DPC++/C++
 Compiler
 attempts to improve performance by automatically banking a local memory system. The compiler includes advanced features that allow you to customize the banking geometry of your local memory system. To configure the geometry of local memory banks, include
numbanks(N)
 and 
bankwidth(M)
 kernel attributes in your SYCL* kernel.
The table provides code examples illustrating how the bank geometry changes based on the values you assign to 
numbanks
 and 
bankwidth
. The first and last rows of this table illustrate how to bank memory on the upper and lower indexes of a 2D array, respectively.
Effects of
numbanks
 and
bankwidth
 on the Bank Geometry of 2 x 4 Local Memory System
Code Example
Bank Geometry 
[[intel::numbanks(2), 
intel::bankwidth(16)]] 
int lmem[2][4];
[[intel::numbanks(2), 
intel::bankwidth(8)]] 
int lmem[2][4];
[[intel::numbanks(2), 
intel::bankwidth(4)]] 
int lmem[2][4];
[[intel::numbanks(4), 
intel::bankwidth(8)]] 
int lmem[2][4];
[[intel::numbanks(4), 
intel::bankwidth(4)]] 
int lmem[2][4];

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.