Introduction

Fast Exact Summation Using Small and Large Superaccumulators

[4pt] Dept. of Statistical Sciences and Dept. of Computer Science

University of Toronto

[4pt] 20 May 2015

I present two new methods for exactly summing a set of floating-point numbers, and then correctly rounding to the nearest floating-point number. Higher accuracy than simple summation (rounding after each addition) is important in many applications, such as finding the sample mean of data. Exact summation also guarantees identical results with parallel and serial implementations, since the exact sum is independent of order. The new methods use variations on the concept of a “superaccumulator” — a large fixed-point number that can exactly represent the sum of any reasonable number of floating-point values. One method uses a “small” superaccumulator with sixty-seven 64-bit chunks, each with 32-bit overlap with the next chunk, allowing carry propagation to be done infrequently. The small superaccumulator is used alone when summing a small number of terms. For big summations, a “large” superaccumulator is used as well. It consists of 4096 64-bit chunks, one for every possible combination of exponent bits and sign bit, plus counts of when each chunk needs to be transferred to the small superaccumulator. To add a term to the large superaccumulator, only a single chunk and its associated count need to be updated, which takes very few instructions if carefully implemented. On modern 64-bit processors, exactly summing a large array using this combination of large and small superaccumulators takes less than twice the time of simple, inexact, ordered summation, with a serial implementation. A parallel implementation using a small number of processor cores can be expected to perform exact summation of large arrays at a speed that reaches the limit imposed by memory bandwidth. Some common methods that attempt to improve accuracy without being exact may therefore be pointless, at least for large summations, since they are slower than computing the sum exactly.

## Introduction

Computing the sum of a set of numbers can produce an inaccurate result if it is done by adding each number in turn to an accumulator with limited precision, with rounding performed on each addition. The final result can be much less accurate than the precision of the accumulator if cancellation occurs between positive and negative terms, or if accuracy is lost when many small numbers are added to a larger number. Such inaccuracies are a problem for many applications, one such being the computation of the sample mean of data in statistical applications.

Much work has been done on trying to improve the accuracy of summation. Some methods aim to somewhat improve accuracy at little computational cost, but do not guarantee that the result is the correctly rounded exact sum. For example, Kahan’s method (Kahan, 1965) tries to compensate for the error in each addition by subtracting this error from the next term before it is added. Another simple method is used by the R language for statistical computation (R Core Team, 1995–2015), which computes the sample mean of data by first computing a tentative mean (adding terms in the obvious way) and then adjusting this tentative mean by adding to it the mean (again, computed in the obvious way) of the difference of each term from the tentative mean. This method sometimes improves accuracy, but can also make the result less accurate. (For example, the R expression mean(c(1e15,-1e15,0.1)) gives a result accurate to only three decimal digits, whereas the obvious method would give the exact mean rounded to about 16 digits of accuracy).

Many methods have been developed that instead compute the exact sum of a set of floating-point values, and then correctly round this exact sum to the closest floating-point value. This obviously would be preferable to any non-exact method, if the exact computation could be done sufficiently quickly.

An additional advantage of exact methods is that they can easily be parallelized, without changing the result, since unlike inexact summation, the exact sum does not depend on the order in which terms are added. In contrast, parallelizing simple summation in the obvious way, by splitting the sum into parts that are summed (inexactly) in parallel, then adding these partial sums, will in general produce a different result than the simple serial method. Furthermore, the result obtained will depend on the details of the parallel algorithm, and perhaps on the run-time availability of processor cores.

Differing results will also arise from serial implementations that do not sum terms in the usual left-to-right order. Such implementations are otherwise attractive, since many modern processors have multiple computational units that can be exploited via instruction-level parallelism, if data dependencies allow it. Summing four numbers as does not allow for any parallelism, but summing them as does, although it may produce a different result. In contrast, focusing on exact computation ensures that any improvements in computational methods will not lead to non-reproducible results.

Exact summation methods fall into two classes — those implemented using standard floating-point arithmetic operations available in hardware on most current processors, such as the methods of Zhu and Hayes (2010), and those that instead perform the summation with integer arithmetic, using a “superaccumulator”. Hybrid methods using both techniques been investigated by Collange, Defour, Graillat, and Iakymchuk (2015a,b).

The methods of this paper can be seen as using “small” and “large” variations on a superaccumulator, though the “large” variation resembles other superaccumulator schemes only distantly. The general concept of a superaccumulator is that it is a fixed-point numerical representation with enough binary digits before and after the binary point that it can represent the sum of any reasonable number of floating-point values exactly and without overflow. Such a scheme is possible because the exponent range in floating number formats is limited.

The idea of such a superaccumulator goes back at least to Kulisch and Miranker (1984), who proposed its use for exact computation of dot products. In that context, the superaccumulator must accommodate the range of possible exponents in a product of two floating-point numbers, which is twice the exponent range of a single floating-point number, and the terms added to the superaccumulator will have twice the precision of a single floating-point number. In this paper, I will consider only the problem of summing individual floating-point values, in the standard (IEEE Computer Society, 2008) 64-bit “double precision” floating-point format, not higher-precision products of such values. Directly extending the methods in this paper to such higher precision sums would require doing arithmetic with 128-bit floating-point and integer numbers, which at present is typically unsupported or slow. In the other direction, exact dot products of “single-precision” (32-bit) floating-point values could be computed using the present implementation, and exact summation of single-precision values could be done even more easily (with smaller superaccumulators).

Below, I first describe the standard floating-point and integer numeric formats assumed by the methods of this paper, and then present the “small” superaccumulator method, whose design incorporates a tradeoff between the largely fixed time for initialization and termination and the additional time used for every term added. I then present a method in which such a small superaccumulator is combined with a “large” superaccumulator. This method has a higher fixed cost, but requires less time per term added.

I evaluate the performance of the small and large superaccumulator methods using a carefully written implementation in C, which is provided as supplementary information to this paper. I compare the performance of these new methods with the obvious (inexact) simple summation method, with a variation on simple summation that accumulates sums of of terms with even and odd indexes separately, allowing for increased instruction-level parallelism, and with the exact iFastSum and OnlineExact methods of Zhu and Hayes (2010), who have provided a C++ implementation. Timing tests are done on sixteen computer systems, that use Intel, AMD, ARM, and Sun processors launched between 2000 and 2013.

The results show that on modern 64-bit processors, when summing many terms (tens of thousands or more), the large superaccumulator method is less than a factor of two slower than simple inexact summation, and is significantly faster than all the other exact methods tested. When summing fewer than about a thousand terms, the small superaccumulator method is faster than the large superaccumulator method. The iFastSum method is almost always slower than the small superaccumulator method, except for very small summations (less than about twenty terms), for which it is sometimes slightly faster. The OnlineExact method is about a factor of two slower than the large superaccumulator method on modern 64-bit processors. It is also slower or no faster on older processors, with the exception of 32-bit processors based on the Pentium 4 Netburst architecture, for which it is about a factor of two faster than the large superaccumulator method.

I conclude by discussing the implications of these performance results, and the possibility for further improvements, such as methods designed for small summations (less than 100 terms), methods using multiple processor cores, and implementations of methods in carefully-tuned assembly language.

## Floating-point and integer formats

The methods in this paper are designed to work with floating-point numbers in the standard (IEEE Computer Society, 2008) 64-bit “double-precision” format, which is today universally available, in hardware implementations, on general-purpose computers, and used for the C language double data type.

Numbers in this format, illustrated in Figure 1, consist of a sign bit, , 11 exponent bits, , and 52 mantissa bits, . Interpreting each group of bits as an integer in binary notation, when is not 0 and not 2047, the number represented by these bits is . That is, represents the binary exponent, with a bias of 1023, and the full mantissa consists of an implicit 1 followed by the bits of . When is 0 (indicating a “denormalized” number), the number represented is . That is, the true exponent is , and the mantissa does not include an implicit 1. A value for of 2047 indicates plus or minus “infinity” when is zero, and a special NaN (“Not a Number”) value otherwise. Note that the smallest non-zero floating-point value is and the largest non-infinite value is .

I also assume that unsigned and signed (two’s complement) 64-bit integer formats are available, and are accessible from C by the uint64_t and int64_t data types. These formats are today universally available for general purpose computers, and accessible from C in implementations compliant with the C99 standard. Arithmetic on 64-bit quantities is well-supported by recent 64-bit processors, but even on older 32-bit processors, addition, subtraction, and shifting of 64-bit quantities are not extraordinarily slow, being facilitated by instructions such as “add with carry”.

Finally, I assume that the byte ordering of 64-bit floating-point values and 64-bit integers is consistent, so that a C union type with double, int64_t, and uint64_t fields will allow access to the sign, exponent, and mantissa of a 64-bit floating-point value stored into the double field via shift and mask operations on the 64-bit signed and unsigned integer fields. Such consistent “endianness” is not guaranteed by any standard, but seems to be nearly universal on today’s computers (of both “big endian” and “little endian” varieties) — including Intel x86, SPARC, and modern ARM processors (though it appears some past ARM architectures may not have been consistent).

## Exact summation using a small superaccumulator

I first present a new summation method using a relatively small superaccumulator, which will prove to be the preferred method for summing a moderate number of terms, and which is also a component of the large superaccumulator method presented below. The details of this scheme are designed for fast implementation in software, in contrast to some other designs (eg, Kulisch, 2011) that are meant primarily for hardware implementation.

The most obvious design of a superaccumulator for use in summing 64-bit floating-point values would be a fixed-point binary number consisting of a sign bit, bits to the left of the binary point, where is the maximum number of terms that might be summed, and bits to the right of the binary point. The bits of such a superaccumulator could be stored in around 34 64-bit words.

However, this representation has several disadvantages. When adding a term to the superaccumulator, carries might propagate through several 64-bit words, requiring a loop in the time-critical addition operation. Furthermore, this sign-magnitude representation requires that addition and subtraction be handled separately, with the sign changing as necessary, necessitating additional complexities. If the superaccumulator instead represents negative numbers in two’s complement form, additions that change the sign of the sum will need to alter all the higher-order bits.

Carry propagation can be sped up using a somewhat redundant “carry-save” representation, in which the high-order bits of each 64-bit “chunk” of the superaccumulator overlap the low-order bits of the next higher chunk, allowing carry propagation to be deferred for some time. This approach is used, for example, by Collange, et al. (2015a,b), whose chunks have 8-bit overlap. In the scheme of Collange, et al., chunks can apparently also have different signs, an arrangement that can alleviate the problems of representing negative numbers, by allowing local updates without the need to determine the overall sign of the number immediately.

In the design I use here, the small superaccumulator consists of 67 signed (two’s complement) 64-bit chunks, with 32-bit overlap. Chunks are indexed starting at 0 for the lowest-order chunk. Denoting the value of chunk as , the number represented by the superaccumulator is defined to be

 66∑i=0ci232i−1075

The will always be in the range to . For convenience, the representation is further restricted so that the highest-order chunk (for ) is in the range to . This representation is diagrammed in Figure 2.

The largest number representable in this superaccumulator is . It can therefore represent any sum of up to terms, which would occupy more than 281 terabytes of memory. This capacity to represent values beyond the exponent range of the 64-bit floating-point format ensures that the final rounded 64-bit floating-point sum obtained using the superaccumulator will be finite whenever the final exact sum is within range, even when summing the values in the ordinary way would have produced overflow for an intermediate result. This is an advantage over methods such as those of Zhu and Hayes (2010), which use floating-point arithmetic, and hence cannot bypass temporary overflows.

Due to the overlap of chunks, and the possibility that they have different signs, a single number can have many possible representations in the superaccumulator. However, a canonical form is produced when carry propagation is done, which happens periodically when adding terms to the superaccumulator, and whenever a floating-point number that is the correct rounding of the superaccumulator’s value is needed. Carry propagation starts at the low order chunk (), and proceeds by clearing the high-order 32-bits of each chunk to zero, and adding these bits (regarded as a signed integer) to the next-higher chunk. The process ends when we reach the highest-order chunk, whose high-order 32 bits will be either all 0s or all 1s, depending on whether the number is positive or negative. Note that all chunks other than this highest-order chunk are positive after carry propagation.

If carried out as just described, carry propagation for a negative number could require modification of many higher-order chunks, all of which would be set to (ie, all 1s in two’s complement). To avoid this inefficiency, the procedure is modified so that such high-order chunks that would have value are instead set to zero, and the upper 32-bits of the next-lower chunk are set to all 1s (so that it is now negative), which produces the same represented number.

After carry propagation, all chunks will be no larger than in absolute value. In the procedure described next for adding a floating-point value to the superaccumulator, the amount added to (or subtracted from) any chunk is at most . It follows that the values of all chunks are guaranteed to remain within their allowed range if no more than additions are done between calls of the carry propagation routine. This is sufficiently large that it makes sense to keep only a global count of remaining additions before carry propagation is needed, rather than keeping counts for each chunk, or detecting actual overflow when adding to or subtracting from a chunk. Using only a global count will result in carry propagation being done more often than necessary, but since the cost of carry propagation should be only a few tens of instructions per chunk, reducing calls to the carry propagation routine cannot justify even one additional instruction in the time-critical addition procedure.

Addition of a 64-bit floating-point value to the superaccumulator starts with extraction of the 11 exponent bits and 52 mantissa bits, using shift and mask operations that treat the value as a 64-bit integer. Note that the sign of the floating-point number is the same as the sign of its 64-bit integer form, so no extraction of the sign bit is necessary.

If the exponent bits are all 1s, the floating-point value is an infinity or a NaN, which are handled specially by storing indicators in auxiliary Inf and NaN fields of the superaccumulator. This operation is typically not highly time-critical, since Inf and NaN operands are expected to be fairly infrequent.

If the exponent bits are all 0s, the floating-point value is a zero or a non-zero denormalized number. If it is zero, the addition operation is complete, since nothing need be done to add zero. Otherwise, the exponent is changed to 1, since this is the true exponent (with bias) of denormalized numbers.

If the exponent bits are neither all 0s nor all 1s, the value is an ordinary normalized number. In this case, the implicit 1 bit that is part of the mantissa value is explicitly set, so that the mantissa value now contains 53 bits.

Further shift and mask operations separate the exponent into its high-order 6 bits and low-order 5 bits. The high-order exponent bits, denoted , index one of the first 64 chunks of the superaccumulator. Chunks and will be modified by adding or subtracting bits of the mantissa. Due to the overlap of these chunks, this could be done in several ways, but it seems easiest to modify chunk by adding or subtracting a 32-bit value, and to use the remaining bits to modify chunk .

In detail, the quantity to add to or subtract from chunk is found by shifting the 53-bit mantissa left by the number of bits given by the low-order 5 bits of the exponent, and then masking out only the low-order 32 bits. The shift positions these mantissa bits to their proper place in the superaccumulator. The quantity to add to or subtract from chunk is found by shifting the 53-bit mantissa right by 32 minus the amount of the previous shift. This isolates (without need of a masking operation) the bits that were not used to modify chunk , positioning them properly for adding to or subtracting from chunk . Note that this quantity will have at most 52 bits, since at least 1 mantissa bit will be used to modify chunk .

When modifying both chunk and chunk , whether to add or subtract is determined by the sign of the number being added. Note that it is quite possible for different chunks to end up with different signs after several terms have been added, but the overall sign of the number is resolved when carry propagation is done.

The C code used for this addition operation is shown in Figure 3. A function that sums an array would use this code (expanded from an inline function) in its inner loop that steps through array elements. This summation function must call the carry propagation routine after every 2047 additions. This is most easily done with nested loops, with the inner loop adding numbers until some limit is reached, which is the same form as the inner loop would be if no check for carry propagation were needed.

Once all terms have been added to the small superaccumulator, a correctly rounded value for the sum can be obtained, after first performing carry propagation. Special Inf and NaN values must be handled specially. Otherwise, the chunks are examined starting at the highest-order non-zero chunk, and proceeding to lower-order chunks as necessary. Note that the sign of the rounded value is given by the sign of the highest-order chunk.

Denormalized numbers are easy to identify, and do not require rounding.

For normalized numbers, a tentative exponent for the rounded value can be obtained by converting the highest chunk’s integer value to floating point, and then looking at the exponent of the converted value. This is the only use of a floating-point operation in the superaccumulator routines. If desired, this operation could be replaced with some other method of finding the topmost 1 bit in a 32-bit word (for instance, binary search using masks). This tentative exponent allows construction of a tentative mantissa from the highest-order chunk and the next lower one or two chunks. Chunks of lower order may need to be examined in order to produce a correctly rounded result, potentially all the way to the lowest-order chunk. Rounding may change the final exponent.

See the code in the supplemental information for further (somewhat finicky) details of rounding. At present, only the commonly-used “round to nearest, with ties to even” rounding mode is implemented, but implementing other rounding modes would be straightforward.

Exact summation using the small superaccumulator has a fixed cost, due to the need to set all 67 chunks to zero initially, and to scan all chunks when carry propagating in order to produce the final rounded result. As will be seen from the experiments below, this fixed cost is roughly 12.5 times the cost of adding a single term to the superaccumulator. A naive count of operations in the C code of Figure 3 gives about 19 operations to add a term to the superaccumulator, compared to 2 operations (fetch and add) for simple floating-point summation. The actual per-term time ratio is not that bad on modern 64-bit processors, probably because these processors can exploit instruction-level parallelism. Nevertheless, to obtain good performance for large summations, we are motivated to look for a scheme with smaller cost per term, even if this increases fixed overhead.

## Faster exact summation of many terms with a large superaccumulator

To reduce the per-term cost of summing values with a superaccumulator, we would like to eliminate from the inner summation loop the operations of testing for special Inf or NaN values, checking the sign of the term in order to decide whether to add or subtract, and splitting the mantissa bits into two parts, so they can be added to different chunks. This can be accomplished by using a large superaccumulator that has 4096 64-bit chunks, one for every possible combination of sign and exponent bits, as well as 4096 16-bit counts, one for each chunk. We still use a small superaccumulator as well, transferring partial sums from the large superaccumulator to the small superaccumulator as necessary to avoid loss of information from overflow. The counts in the large superaccumulator are all initialized to ; the chunks are not set initially.

The C code for adding a value to this large superaccumulator is shown in Figure 4. It starts by isolating the sign and exponent bits of the floating-point value, viewed as an unsigned 64-bit integer, by doing a right shift by 52 bits (with zero fill, so no masking is needed). These 12 bits will be used to index a 64-bit chunk of the large superaccumulator, and the corresponding 16-bit count.

The count indexed by the sign and exponent is then fetched, and decremented. If this decremented count is non-negative, it is stored as the new value for this count, and the entire floating-point value is added, as a 64-bit integer, to the 64-bit chunk indexed by the sign and exponent. Note that no operation to mask out just the mantissa bits of this value is done. This masking can be omitted because the undesired bits at the top are the same for every add to any particular chunk, and are known from the index of that chunk. Since the number of adds that have been done is also kept track of in the count, the effect of adding these bits can be undone before transferring the sum held in the chunk to the small superaccumulator.

If instead the decremented count is negative, a routine to do special processing is called. This test merges a check for the value being an Inf or NaN, a check for the indexed chunk having not yet been initialized, and a check for having already done the maximum allowed number (4096) of adds to the indexed chunk, so that the chunk’s contents must now be transferred to the small superaccumulator.

Since all these circumstances are expected to arise infrequently, the special processing routine is not time-critical. It operates as follows. When the exponent bits in the index passed to this routine are all 1s, the value being added is an Inf or NaN, which is handled by setting special fields of the small superaccumulator associated with this large superaccumulator. The count for this index remains at , so that subsequent adds of this Inf or NaN will also be processed specially. For other exponents, if the count (before being decremented) is , indicating that this is the first use of this chunk, the chunk is initialized to zero, and the count is set to 4096. Otherwise, the count must be zero, indicating that the maximum of 4096 adds have previously been done to this chunk, in which case the sum is transferred to the small superaccumulator, the chunk is reset to zero, and the count is reset to 4096. In the latter two cases, the addition then proceeds as usual (adding to the chunk and decrementing the count).

The partial sum in a large superaccumulator chunk will need to be transferred to the small superaccumulator when the maximum number of adds before overflow has already been done, or when the final rounded result is desired. When the maximum of 4096 adds has been done, the bits in the chunk are the correct sum of mantissa bits, without any further adjustment, since adding the same sign and exponent bits 4096 times is the same as multiplying by 4096, which is the same as shifting these bits left 12 positions, which removes them from the 64-bit word. When the transfer to the small superaccumulator is done before 4096 adds to the chunk, we need to add to the chunk the the chunk’s index (the sign and exponent bits) times the count of remaining allowed adds, shifted left 52 bits, which has the effect of leaving only the sum of mantissa bits.

The sum of the mantissa bits for all values that were added to this chunk has unsigned magnitude up to , so all 64 bits of the chunk are used. There would be several ways of transferring these bits to the small superaccumulator, but it seems easiest to do so by modifying three consecutive small superaccumulator chunks by adding or subtracting 32-bit quantities. Conceptually, these three 32-bit quantities are obtained by shifting the 64-bit chunk left by the number of positions given by the low 5 bits of the exponent (the same as the low 5 bits of the chunk index), and then extracting the lowest 32 bits, the next 32 bits, and the highest 32 bits. However, since shift operations on quantities greater than 64 bits in size may not be available, the equivalent result is instead found using a some left and some right shifts, and suitable masking operations. For chunks corresponding to normalized floating-point values (ie, for which the exponent is not zero), we also add in the sum of all the implicit 1 bits at the top of the mantissa (which would be beyond the top of the 64-bit chunk) to the appropriate 32-bit quantity. Finally we either add or subtract these three 32-bit quantities from the corresponding chunks of the small superaccumulator according to the sign bit, which is the top bit of the 12-bit index of the chunk.

The fixed cost of summation using a large superaccumulator is greater than that of using only a small superaccumulator because of the need to initialize the array of 4096 counts, occupying 8192 bytes. Note this is in addition to the fixed costs of using the small superaccumulator, which is still needed as well. Note, though, that the 4096 large superaccumulator chunks, occupying 32768 bytes, are not initialized, but instead are set to zero only when actually used. For many applications, it will be typical for only a small fraction of the chunks to be used, because the numbers summed have limited range, or are all the same sign.

It is also necessary to transfer all large superaccumulator chunks to the small superaccumulator when the final rounded result is required. The obvious way of doing this would be to look at all 4096 counts, transferring the corresponding chunk if the count is not . The overhead of this can be reduced by keeping an array of 64 flag words, each a 64-bit unsigned integer, whose bits indicate which chunks have been used. These flag words can be used to quickly skip large regions of unused chunks. This scan can be further sped up, in many cases, using a 64-bit unsigned integer whose bits indicate which of the 64 flag words are not all zero. Maintaining these flag words slightly increases the cost of processing a chunk when its count is negative, but does not increase the cost of the inner summation loop.

A naive count of operations for adding one term in the C code of Figure 4 gives only about 8, compared to about 19 in Figure 3. And indeed, we will see below that summing large arrays using a large superaccumulator is about twice as fast as summing them using only a small superaccumulator.

## Performance evaluations

The relative performance of different method for summing the elements in an array will depend on many factors. Some concern the problem instance — such as the number of terms summed, and the range of numerical values spanned by those terms. Others concern the computing environment — such as the architecture of the processor, the speed of memory, and which compiler is used. There will also be random noise in measurements.

The resulting variability has led Langlois, Parello, Goossens, and Porada (2012) to despair of obtaining meaningful times on real machines, and to instead advocate assessing exact summation methods based on reproducible measurements from a simulation of how long a program would run on a hypothetical ideal processor in which instruction-level parallelism (ILP) allows each operation to be performed as soon as the operands it depends on have been computed. While this work does provide insight into the methods they assess, it does not answer the practical question of how well the methods perform on real computers.

Here, I will use time measurements on real computer systems to assess performance in a way that is both directly useful and provides some insight into the factors affecting performance of summation methods. Sixteen computer systems with a variety of characteristics were used, many of them in conjunction with several compilers.

I limited the scope of this assessment to serial implementations. Although many of the processors used have multiple cores or threads, only a single thread was executing during these tests. (The systems were largely idle apart from the test program itself.)

The small and large superaccumulator methods were implemented in C, with careful attention to efficiency. Several code segments were implemented twice, once in a straightforward manner (without obvious inefficiencies), and a second time with attempts at manual optimizations, such as loop unrolling and branch avoidance. The straightforward implementation might be the most efficient, if the compiler produces superior optimization decisions. This was not found to be the case, however, so the manually-optimized versions were used.

The simple summation routines were similarly implemented (with manually optimized versions chosen). The ordered summation routine adds each term in turn to a 64-bit double-precision accumulator. The unordered summation routine uses separate accumulators for terms with even and odd indexes, then adds them together at the end. This allows scope for instruction-level parallelism.

For the iFastSum and OnlineExact methods of Zhu and Hayes (2010), I used the C++ implementation provided by them as supplementary information to their paper. From casual perusal, this C++ code appears to be a reasonably efficient implementation of these methods, but it is possible that it could be improved.

The C/C++ compilers used were gcc-4.6, gcc-4.7, gcc-4.8, gcc-4.9, clang-3.4, clang-3.5, and clang-3.6. For many of the systems, more than one of these compilers were available. Choice of compiler sometimes had a substantial impact on the performance of the various methods, and the most recent compiler version was not always the best. Since relative as well as absolute performance differed between compilers, an arbitrary choice would not have been appropriate. Instead, for each method a best choice of compiler from among those available was made, based on the time summing 1000 terms for the small superaccumulator and iFastSum methods, on the time summing 10000 terms for the two simple summation methods, and on the time summing 100000 terms for the large superaccumulator and OnlineExact methods. The compiler chosen for each method was then used for summations of all sizes done with that method.

Seven array sizes were tried, ranging from to by powers of ten, which covers the sizes relevant to the sizes of data caches in the processors tested. This range of sizes also shows the effects of fixed versus per term costs for the various methods. Each summation was repeated times, and the total time for all summations was recorded, along with the total time divided by the total number of terms summed (which was always ), which was reported in nanoseconds per term, and is what is shown in the plots below.

Note that due to the -fold repetition, with at least 10, summing arrays of a size for which all the data fits in the memory cache should result in most memory accesses being to cache. The processors used all have at least two levels of cache, whose sizes are shown by vertical lines in the plots, at the number of terms for which the data would just fit in that level cache.

In order to limit the effort needed for this assessment, I mostly used only a single distribution for numeric elements of the arrays summed, as follows. The terms in the first half of each array that was summed were independent, with values given by , with and being pseudo-random values uniformly drawn from using a multiplicative congruential generator with period 67101322. (The standard C rand generator was avoided, since it is not the same on all systems.) The terms in the second half of the array were the negations of the mirror reflection of the terms in the first half — that is, element was the negation of element . The exact sum of all terms was therefore zero. I also performed a few tests in which the elements of the array were randomly permuted before being summed, as discussed after the main results shown in the figures.

Figures 5 though 9 show the results of the performance tests, with the six methods indicated by colour and solid vs. dashed lines as shown in the key above Figure 5. The processor manufacturer, model, and year of release are show above each plot.

Performance on six 64-bit Intel systems and two 64-bit AMD systems (which use the Intel Instruction Set Architecture) is shown in Figures 5 and 6. The Xeon and Opteron processors are designed for use in servers and high-end workstations. The Intel Core 2 Duo is from an Apple MacBook Pro, the Intel Celeron 1019Y is from a low-end Acer AspireV5 laptop, and the AMD E1-2500 is from a low-end Gateway desktop system. The six Intel processors span three major microarchitecture families — “Core” (Core 2 Duo, Xeon E5462), “Nehalem” (X5680), and “Sandy/Ivy Bridge” (Xeon E3-1225, Xeon E3-1230 v2, and Celeron 1019Y). The two AMD processors also have different microarchitectures — “Piledriver” (Opteron 6348) and “Jaguar” (E1-2500).

The qualitative picture from these tests on modern processors is quite consistent. The large superaccumulator method is faster than the small superaccumulator method when summing more than about 1000 terms. Similarly, the OnlineExact method is faster than the iFastSum method when summing more than about 2500 terms. The combination of the two superaccumulator methods — the small superaccumulator method for less than 1000 terms, and the large superaccumulator method for 1000 terms or more — is superior to any combination of the iFastSum and OnlineExact methods, except that for some processors iFastSum is slightly faster when summing very small arrays (less than about thirty terms, or less a few hundred terms for the AMD Opteron 6348 processor).

The advantage of the large superaccumulator method over the OnlineExact method for summing a large number of terms (10000 or more) is about a factor of two, except that for some processors this decreases (to nothing for the AMD Opteron 6348) when summing very large arrays, for which out-of-cache memory access time dominates. The advantage of the small superaccumulator method over iFastSum when summing small arrays is less (non-existent for the Intel Core 2 Duo and the AMD Opteron 6348), but the small superaccumulator method nevertheless appears to be generally preferable to iFastSum for other than very small sums.

The large superaccumulator method is no more than about a factor of two slower than simple ordered summation, when summing 10000 or more terms. For the AMD Opteron 6348, the large superaccumulator method is only slightly slower than simple summation, though for the AMD E1-2500 the ratio of times is slightly greater than two. The difference between the large superaccumulator method and simple ordered summation is often less for very large summations, as expected if time for out-of-cache memory accesses starts to dominate.

The simple summation method that adds terms out of order is about twice as fast as simple ordered summation, except for summing very large arrays, for which its advantage is usually less (sometimes non-existent). For array sizes of 10000 and 100000, simple unordered summation is typically about three times faster than the large superaccumulator method.

How the methods perform on four 32-bit Intel processors is shown in Figure 7. The Intel Pentium III processor uses the “P6” microarchitecture, which is a distant ancestor of the “Core” microarchitecture of the Intel Xeon X5355. The Intel Xeon (1.7 GHz) and Intel Pentium 4 use the “NetBurst” microarchitecture. The Intel Pentium III processor uses the 387 floating-point unit for floating-point arithmetic, whereas the other processors have the SSE2 floating-point instructions, which have more potential for instruction-level parallelism.

As was the case for the 64-bit processors, we see that for summing large arrays, the large superaccumulator method is better than the small superaccumulator method, and the OnlineExact method is better than the iFastSum method. The combination of small and large superaccumulator methods is better than the combination of iFastSum and OnlineExact for the Intel Pentium III, except for very small arrays. The advantage of the large superaccumulator method over OnlineExact is a factor of two when summing 10000 terms, but is not as large for 1000 terms (probably because of fixed overhead) or for 100000 terms (probably because out-of-cache memory access time starts to dominate). For the Intel Xeon X5355, the large superaccumulator method has only a slight advantage when summing 1000 terms, and the superaccumulator methods perform almost identically to iFastSum+OnlineExact for other sizes (with the small superaccumulator method being slower than iFastSum for very small sums).

For the two processors with “NetBurst” microarchitecture – the Intel Xeon at 1.7 GHz and the Intel Pentium 4 — the picture is quite different. For these processors, the combination of iFastSum and OnlineExact is better than the combination of the small and large superaccumulator methods for all array sizes. The advantage of OnlineExact over the large superaccumulator method is almost a factor of two for summing large arrays. For smaller arrays, there is less difference between the methods. One might speculate that this reflects a design emphasis on floating-point rather than integer performance in the “NetBurst” processors. One can see that for summing 10000 terms, both the small and large superaccumulator methods are actually slower on the 1.7 GHz Xeon than on the 1 GHz Pentium III, whereas both simple summation and the OnlineExact method perform substantially better.

For all these 32-bit Intel processors, the ratios of the times for the exact summation methods to the times for simple summation are substantially greater than for the 64-bit processors (though less so for very large summations, where out-of-cache memory access time is large). This also may reflect a somewhat specialized design philosophy for these processors, in which general-purpose computation was supported only with 32-bit registers and operations, whereas support for 64-bit floating-point computations was similar to that found in modern 64-bit processors.

Figure 6 shows result on two 32-bit ARM processors. For the ARMv6 processor, the combination of small and large superaccumulator methods performs better than iFastSum+OnlineExact, but for the Cortex-A9 ARMv7 processor the comparison is mixed. The exact summation methods are again slower compared to simple summation than is the case for the modern 64-bit processors.

Finally, Figure 9 shows results for two UltraSPARC 64-bit processors. For both processors, the combination of the small and large superaccumulator methods performs significantly better than iFastSum+OnlineExact. The performance of the superaccumulator methods is slower compared to simple summation for these processors than for the 64-bit Intel and AMD processors. One should note that the UltraSPARC T2 Plus is optimized for multi-threaded workloads, with 8 threads per core, so a performance comparison using a single thread, as here, may be misleading.

One can measure the fixed overhead of the small superaccumulator method by looking at the ratio of the time per term for 10 terms and for 100 terms. This ratio is roughly 2 for most of the processors tested. Assuming that the time for a summation can be modelled as , where is the number of terms, is the fixed cost, and is the per term cost, one can work out that is about 12.5, as was mentioned earlier. This model does not work well for the large superaccumulator method, perhaps because the “fixed” overhead is not actually fixed when is small, since the number of chunks used in the large superaccumulator will grow at a substantial rate with the number of terms summed when is still fairly small.

I did a few timing runs in which the elements of the arrays were randomly permuted before summing them. This has the effect of mixing positive and negative terms randomly (rather than all positive terms coming before all negative terms), and also affects the contents of the superaccumulators at intermediate stages. This permutation had little effect on the performance of the large superaccumulator and OnlineExact methods. However, it did increase the time for the small superaccumulator method on some processors, including recent ones. This is perhaps due to the conditional branch in the inner loop shown in Figure 3, which can be well-predicted if many terms in a row have the same sign, but not if positive and negative terms are mixed, which will affect the time on processors that do speculative execution of instructions that may follow a branch. The time for the iFastSum method was not affected as much, so the relative advantage of the small superaccumulator method over iFastSum was smaller, though the small superaccumulator method was still faster when summing at least 100 terms.

Kahan’s (1965) method for reducing summation error (but without producing the exact result) was also tested on all systems. On modern 64-bit processors, computing the exact sum with the large superaccumulator method was faster than Kahan’s method for summations of more than about 1000 terms. Kahan’s method was significantly faster than the small superaccumulator method only for summations of less than about 100 terms.

I also implemented functions for computing the squared norm of a vector (sum of squares of elements) and the dot product of two vectors (sum of products of corresponding elements) using the small and large superaccumulator methods for the summations. (The products were computed as usual, with rounding to the nearest 64-bit double-precision floating point number.) I compared these implementations with versions using simple ordered and unordered summation. The results for the Intel E3-1230 v2 and the AMD Opteron 6348 are shown in Figures 10 and 11.

The times shown in these figures are somewhat disappointing. Considering that the inner loops of the superaccumulator methods make no use of the processor’s floating-point instructions, I had hoped that the multiplications in these functions would be executed in parallel with the integer operations on the superaccumulator, with the result that the squared norm of a vector would be computed in no more time than required for summing its elements (and similarly for the dot product, if the two vectors remain in cache memory). This is true for the small superaccumulator method on the AMD Opteron 6348, but for the large superaccumulator method on this processor, and for both superaccumulator methods on the Intel E3-1230 v2, the time required for computing the squared norm is noticeably greater than the time for summing the elements with the same method. In contrast the times for computing the squared norm using simple ordered and unordered summation are indistinguishable from the times for simple summation, for vectors of length 1000 or more. The picture is the same for computation of the dot product, until the greater memory required becomes a factor for large vectors.

The reason for this worse than expected performance is not apparent, but one might speculate that the compilers simply fail to arrange instructions in a manner that would allow for exploitation of the instruction-level parallelism that would seem to be possible. Note, however, that for large vectors the times to compute the squared norm or dot product with exact summation are nevertheless still less than a factor of two greater than the times using simple ordered summation.

More information on these performance assessments, including details of the computer systems and compilers used, is included in the supplementary information for this paper.

## Discussion

On modern 64-bit processors, serial implementations of the two new exact summation methods introduced in this paper dominate, in combination, what appears to be the best combination of previous exact summation methods — the iFastSum and OnlineExact methods of Zhu and Hayes (2010). The advantage is typically about a factor of two for large summations. Note also that the superaccumulator methods produce a finite final result whenever the correct rounding of the exact sum is representable as a finite 64-bit floating-point number, whereas the methods of Zhu and Hayes may produce overflow even when the final result can be represented.

With the improvement in performance obtained with these superaccumulator methods, exact summation is less than a factor of two slower than simple ordered summation, and about a factor of three slower than simple unordered summation, when summing more than a few thousand terms. For large vectors, computing the sum exactly is faster than attempting to reduce (but not eliminate) error using Kahan’s (1965) method, and Kahan’s method has a significant speed advantage only when the number of terms is less than about one hundred.

For many applications, the modest extra cost of computing the exactly-rounded sum may be well worth paying, in return for the advantages of accuracy. Exact summation also has the natural advantage of being reproducible on any computer system that uses standard floating point, unlike the situation when a variety of unordered summation methods are used.

The implementation of the small and large superaccumulator methods can probably be improved. In the inner loop of the small superaccumulator method, the conditional branch testing whether a term is positive or negative could be eliminated (shifting the term right to produce all 0s or all 1s, then XOR’ing to conditionally negate), although this might be slower when the terms actually all have the same sign. The significant variation in performance seen with different compilers may indicate that none of them are producing close to optimal code. Future compiler improvements might therefore speed up the performance of the exact summation methods. Alternatively, it seems likely that performance could be improved by rewriting the routines in assembly language.

One would also expect that using more than one processor core would allow for faster exact summation. Collange, Defour, Graillat, and Iakymchuk (2015a,b) and Chohra, Langlois, and Parello (2015) both describe parallel implementations of exact summation. Although these authors consider a variety of parallel architectures, I will limit discussion here to parallelizing exact summation on a shared memory system with multiple general-purpose processor cores or threads.

In this context, any exact summation method can be parallelized in a straightforward way by simply splitting the array to be summed into parts, summing each part in parallel (retaining the full exact sum) and then adding together the partial sums before finally rounding to a single 64-bit floating point number. For the methods of this paper, this would require writing a routine to add together two small superaccumulators, a straightforward operation that would take time comparable to that for producing the final rounded result from a small superaccumulator. Of course, it is possible that more integrated algorithms might be somewhat faster, but for large summations, this simple approach should exploit most of the possible parallelism available from using a modest number of cores (eg, the two to eight cores typical on current workstations).

For very large summations, the results in Figures 5 and 6 suggest that only a few cores will be needed to reach the limits imposed by memory bandwidth. For example, the Intel Xeon E3-1230 v2 processor has a maximum memory bandwidth of 25.6 GBytes/s, which is 0.3125 ns per 8-byte floating-point value. When summing arrays of elements, the large superaccumulator method takes 1.16 ns/term, which is 3.7 times larger than the limit imposed by memory bandwidth, suggesting that 4 cores would be enough to sum terms at the maximum possible rate. Since the bandwidth achievable in practice is probably less than the theoretical maximum, it may be that fewer than 4 cores or threads would suffice. (The E3-1230 v2 processor has 4 cores, each of which can run 2 threads.)

The issue is more complex for summations of around to terms, which may well reside in faster cache memory, which may or may not be shared between cores. Experimental evaluations seem essential to investigating the limits of parallel summation in this regime.

One should note that when comparing methods that all produce the exact result, and all do so at the maximum rate, limited by memory bandwidth, the methods can still be distinguished by how many cores or threads they use in order to achieve this. This is an important consideration in the context of a whole application that runs other threads as well, and in the wider context of a computer system performing several jobs,

The small superaccumulator method, as well as iFastSum, are rather slow when summing only a few terms, being ten to twenty times slower than simple ordered summation. The small superaccumulator method sets 67 8-byte chunks to zero on initialization, and must scan them all when producing a rounded result. This fixed cost dominates the per term cost when summing only a few terms. This will limit use of exact summation in applications where many small summations are done, which might be of as few as three terms. (Sums of two terms are exactly rounded with standard floating-point arithmetic.)

Several approaches could be considered for reducing this fixed overhead. One might replace the full array of 67 chunks with a small list of the non-zero chunks. Or one might instead keep track of which chunks are non-zero in a bit array, foregoing actually setting the value of a chunk until it becomes non-zero, and also using these bits to quickly locate the non-zero chunks when producing the final rounded result. These approaches would increase the cost per term, so the current small superaccumulator method would probably still be the fastest method for moderate-size summations.

My original motivation for considering exact summation was improving the accuracy of the sample mean computation in R. In this application, the overhead of calling the mean function in the interpretive R implementation will dominate the fixed overhead of the small superaccumulator method, so finding a faster method for very small summations is not essential.

Computing the sample mean by computing the exactly rounded sum of the data items and then diving by the number of items will not produce the correct rounding of the exact sample mean, though it will be quite close (assuming overflow does not occur). However, it should be straightforward to write a function that directly produces the correct rounding of the value in a small superaccumulator divided by a positive integer. I plan to soon implement such an exact sample mean computation in my pqR implementation of R (Neal, 2013–2015).

## Acknowledgements

This research was supported by Natural Sciences and Engineering Research Council of Canada. The author holds a Canada Research Chair in Statistics and Machine Learning.

## References

Chohra, C., Langlois, P., and Parello, D. (2015) “Efficiency of reproducible level 1 BLAS”. SCAN 2014 Post-Conference Proceedings, 10 pages.
http://hal-lirmm.ccsd.cnrs.fr/lirmm-01101723

Collange, S., Defour, D., Graillat, S., and Iakymchuk, R. (2015a) “Full-speed deterministic bit-accurate parallel floating-point summation on multi- and many-core architectures”, 11 pages.
https://hal.archives-ouvertes.fr/hal-00949355v2

Collange, S., Defour, D., Graillat, S., and Iakymchuk, R. (2015b) “Numerical reproducibility for the parallel reduction on multi- and many-core architectures”, 14 pages.
https://hal.archives-ouvertes.fr/hal-00949355v3

IEEE Computer Society (2008) IEEE Standard for Floating-Point Arithmetic.

Kahan, W. (1965), “Further remarks on reducing truncation errors”, Communications of the ACM, vol. 8, p. 40.

Kulisch, U. W. and Miranker, W. L. (1984) “The arithmetic of the digital computer: A new approach”, SIAM Review, vol. 28, pp. 1–40.

Kulisch, U. (2011) “Very fast and exact accumulation of products”, Computing, vol. 91, pp. 397-405.

Langlois, P., Parello, D., Goossens, B., and Porada, K. (2012) “Less hazardous and more scientific research for summation algorithm computing times, 34 pages.
http://hal-lirmm.ccsd.cnrs.fr/lirmm-00737617

Neal, R. M. (2013–2015) pqR — a pretty quick version of R. http://pqR-project.org

R Core Team (1995–2015) The R Project for Statistical Computing. http://r-project.org

Zhu, Y.-K. and Hayes, W. B. (2010) “Algorithm 908: Online exact summation of floating-point streams”, ACM Transactions on Mathematical Software, vol. 37, article 37, 13 pages, with accompanying software.

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