/ :: bitbashing

Optimizing Forward Error Correction Coding Using SIMD Instructions

Forward error correction (FEC) is a technique for handling lossy storage devices or transmission channels. A FEC code takes k blocks of data and produces an additional m blocks of encoding information, such that any set of k of the blocks (out of the k+m total) is sufficient to recover the original data. One can think of RAID5 as a FEC with arbitrary k and m fixed at 1; most FEC algorithms allow wide latitude for the values that can be sent, allowing the code to be adjusted for the reliability expectations and needs of the particular channel and application. For instance, the Tahoe distributed filesystem splits stored files using k of 3 and m of 7, so as long as at least 30% of the devices storing the file survive, the original file can be recoved.

One of the best known open source FEC implementations is Luigi Rizzo’s fec library, which was later adapted into projects including zfec (the FEC library used in Tahoe) and Onion Network’s Java FEC lib. Rizzo’s fec implements a Reed-Solomon code using Vandermonde matrices, which works by multiplying the input data against a specially constructed matrix in GF (2n). The first k rows of this matrix are I, the identity matrix, which when multiplied against the vector containing the input data, produces exactly the input data. Codes with this property, that the first blocks of encoded data contain an exact bitwise copy of the input, are referred to as systematic codes. The final m rows are constructed in such a way that a matrix formed by choosing any arbitrary k rows of the encoding matrix is invertible (this is equivalent to saying that each row of the matrix is linearly independent from the others)

The reason for wanting to be able to invert the matrix becomes clear once we consider how to decode some set of k shares into the original input data. This set of shares is exactly the data that would have been produced had the original data been multiplied by a k * k matrix containing only the rows corresponding to the surviving shares - thus, multiplying the vector by the inverse of this matrix always results in the original input vector.

It would be possible to implement this algorithm using infinite precision integers, but for efficiency (using only fixed size elements), the matrix elements are taken from a finite field, which normally is chosen to be a power of 2 that matches a CPU wordsize, typically 28 or 216, though Paul Crowley and Sebastian Egner have shown how to efficiently implement finite field arithmetic in GF(232-5). Rizzo’s fec supports a range of field sizes, but others, including zfec, restrict it to 28 since that tends to be the most efficient field to operate in on most computers.

Computing the matrix multiplication requires a series of row-level multiply-add operations. Profiling (using valgrind and kcachegrind) indicated this operation takes up over 90% of the runtime for both encoding and decoding operations. In a finite field of characteristic two, addition and subtraction are both implemented easily as XOR, but multiplication and division are significantly more complex; multiplying two field elements in GF(28) looks like this:

byte gf_mul(byte a, byte b)
   {
   byte product = 0;

   for(size_t counter = 0; counter < 8; ++counter)
      {
      if((b & 1) == 1)
         product ^= a;
      bool hi_bit_set = (a & 0x80);
      a <<= 1;
      if(hi_bit_set)
         a ^= 0x1D; /* x^8 + x^4 + x^3 + x^2 + 1 */
      b >>= 1;
      }

   return product;
   }

Normally, instead the multiplication operation is performed using a precomputed table of exponentials and logarithms, which reduces the problem to a set of table lookups and an addition:

byte gf_mul(byte a, byte b)
   {
   return GF_EXP[(GF_LOG[a] + GF_LOG[b]) % 255];
   }

which is much faster than the loop-based algorithm given above. The reduction modulo 255 is not strictly necessary - since each element of GF_LOG is at most 255, instead you can double out GF_EXP to 510 elements, with the last half of the array simply replicating the first half.

Rizzo’s fec includes an optimization to use a larger 64 kilobyte table which precomputes all byte-by-byte multiplications.

This month I’ve been doing some work with updating Rizzo’s fec with relatively convenient C++ API, as well as optimizing some of the internals. During that process, I realized it is possible to implement GF(28) multiplications in parallel using SSE2 or other SIMD instruction sets.

The trick is to go back to the slower loop based operation. Here was my initial prototype of the addmul inner loop which I used to test the concept:

void addmul(byte z[], const byte x[], byte y, size_t size)
   {
   if(y == 0)
      return;

   const size_t blocks_16 = size - (size % 16);

   const byte polynomial[16] = {
      0x1D, 0x1D, 0x1D, 0x1D, 0x1D, 0x1D, 0x1D, 0x1D,
      0x1D, 0x1D, 0x1D, 0x1D, 0x1D, 0x1D, 0x1D, 0x1D };

   for(size_t i = 0; i != blocks_16; i += 16)
      {
      byte products[16] = { 0 };

      byte x_is[16];
      memcpy(x_is, x + i, 16);

      for(size_t j = 0; j != 8; ++j)
         {
         if((y >> j) & 1)
            for(size_t k = 0; k != 16; ++k) // pxor
               products[k] ^= x_is[k];

         byte mask[16] = { 0 };
         for(size_t k = 0; k != 16; ++k) // maskmovdqu
            if(x_is[k] & 0x80)
               mask[k] = polynomial[k];

         for(size_t k = 0; k != 16; ++k) // paddb
            x_is[k] = x_is[k] + x_is[k];

         for(size_t k = 0; k != 16; ++k) // pxor
            x_is[k] ^= mask[k];

         }

      for(size_t k = 0; k != 16; ++k) // pxor
         z[i+k] ^= products[k];
      }

   for(size_t i = blocks_16; i != size; ++i)
      {
      byte product = 0;
      byte x_i = x[i];

      for(size_t j = 0; j != 8; ++j)
         {
         if((y >> j) & 1)
            product ^= x_i;
         bool high_set = (x_i & 0x80);
         x_i <<= 1;
         if(high_set)
            x_i ^= 0x1D;
         }

      z[i] ^= product;
      }

Comparing the two loops, the equivalence is pretty straightforward. The only confusing parts are the replacement of the right shift by 1 with an addition; SSE2 contains bitshift instructions for operating on 64, 32, and 16 bit elements in a SSE register, but not on bytes. However a right shift by 1 is equivalent to a multiplication by 2, which is the same as adding the same value twice, so the byte-wise addition instruction paddb works nicely.

The other difficult part is that the XOR of the bitwise representation of the polynomial should only occur if the right shift overflowed. Reading through Intel’s processor manuals, I found MASKMOVDQU, which seemed, at first, to be exactly what I wanted:

Stores selected bytes from the source operand (first operand) into a 128-bit memory location. The mask operand (second operand) selects which bytes from the source operand are written to memory. The source and mask operands are SSE registers. The most significant bit in each byte of the mask operand determines whether the corresponding byte in the source operand is written to the corresponding byte location in memory: 0 indicates no write and 1 indicates write.

However tests showed my implementation using Intel intrinsics was about 10 times slower than using a simple 64 kilobyte lookup table. This turned out to be mostly due to my choice of MASKMOVDQU for creating the mask. First, MASKMOVDQU can only write to memory, so I had to create a buffer on the stack, have MASKMOVDQU write to it, and then immediately read it back into a SSE register. Worse, the instruction assumes this memory address is not properly aligned - meaning it is much slower than is necessary, since there is no problem aligning the buffer using GCC’s __attribute__((aligned)) syntax. And third, it writes only to a fixed location in memory, specified by the EDI register. This causes trouble in particular because I wanted to unroll the loop 4 times to process 64 bytes (a L1 cache line’s worth of data) at a time, but the pipeline was probably stalling constantly due to conflicting reads and writes in this memory location.

So, how to generate the mask? What we specifically want here is a SSE register whose bytes are 0 or 0x1D (the primitive polynomial), depending on if the high bits of the bytes in another SSE register are set. The SSE compare instructions seemed promising for this: if the comparison is true for a subword, the instruction will fill the corresponding word in the result with all 1 bits, or otherwise all 0 bits. So combining the result of the right SSE comparison operation with a vector containing 16 0x1D bytes using a bit-wise AND would generate our desired mask.

For various reasons (mostly because I was hacking on it at 2 in the morning and didn’t read Intel’s documentation correctly) I initially believed that I could not use PCMPGTB, the byte-wise greater than comparison operation. So the solution I came up with to generate the mask without using this instruction was to add 0x7F to each byte using saturating arithmetic - if the high bit is set, this will clamp the result to 0xFF, leaving ones that did not have the high bit set with some value between 0x7F and 0xFE. Then by comparing each byte for equality with 0xFF, we generate the mask containing bytes of either all 0 or all 1. Using Intel intrinsics, this looks like:

const __m128i polynomial = _mm_set1_epi8(0x1D);
const __m128i high_bit_set_if_gt = _mm_set1_epi8(0x7F);
const __m128i all_ones = _mm_set1_epi8(0xFF);

[...]

__m128i mask = _mm_adds_epu8(x, high_bit_set_if_gt);
mask = _mm_cmpeq_epi8(mask, all_ones);
mask = _mm_and_si128(mask, polynomial);

This version was much faster than using MASKMOVDQU, but still slower than a table lookup on the x86 processors I have. As this instruction sequence executes 7 times for every 16 bytes of data, I really wanted to shorten this up, and searched through the instruction references for all the x86 SIMD instruction sets in vain; the Wikipedia description of the SSSE3 instruction PSIGNB, that it will “Negate the elements of a register of bytes, words or dwords if the sign of the corresponding elements of another register is negative.” gave me some hope, but it turns out to have some funky semantics which I think prevent it from being useful for this application.

After puzzling over this for a while, I finally realized that the comparison operation actually would work for my purposes, and I changed the mask generation to:

const __m128i polynomial = _mm_set1_epi8(0x1D);
const __m128i all_zeros = _mm_setzero_si128();

[...]

__m128i mask = _mm_cmpgt_epi8(all_zeros, x);
mask = _mm_and_si128(mask, polynomial);

SSE2 only contains signed comparison operators (this is part of what confused me in the first place), so when we check that 0 is greater than x, the result will only be true iff the sign bit (the MSB) of each byte in x is set, which is exactly what we are going for.

Using this sequence to generate the mask, the SSE2 code is significantly faster than using a byte at a time lookup into a 64 kilobyte table of precomputed results on both an Opteron and a Core2 (twice as fast, for large blocks), though it is actually a bit slower on my Pentium4-M laptop. This is a bit disappointing but not hugely surprising - the SSE2 version is doing a great deal more computational work than the lookup table version, so it will only be faster when the ratio of SIMD instructions per clock to memory access latency (in CPU clocks) is sufficiently high. So I suspect SIMD GF(28) multiplications will be a win on processors like the Intel Core2 and i7, the STI Cell, or the PowerPC 970, all of which have incredibly amounts of SIMD horsepower but relatively poor memory latency (in terms of CPU clock cycles), while less SIMD-focused processors (or those with very low memory latencies) will continue to get superior performance using lookup tables. However from my understanding of the current and likely near-future state of processors, the former are going to become much more common than the latter.

I have also experimented with various loop optimizations, such as loop tiling, which is often used to optimize large matrix multiplications, but with no measurable speedups on any platform. I am hoping to revisit this issue later in terms of multithreaded operation, since cache effects may become more important with multiple threads competing for a limited amount of L2 cache.

The full source code of my BSD-licensed implementation is available in fecpp-0.8; there is not much documentation but the readme contains an overview and there are a few example programs, including a zfec-compatible encoder.

Update: I’ve set up a project page for fecpp.