# Linear-log Bucketing: Fast, Versatile, Simple

There’s a couple code snippets in this post (lb.lisp, bucket.lisp, bucket-down.lisp, bin.c). They’re all CC0.

What do memory allocation, histograms, and event scheduling have in common? They all benefit from rounding values to predetermined buckets, and the same bucketing strategy combines acceptable precision with reasonable space usage for a wide range of values. I don’t know if it has a real name; I had to come up with the (confusing) term “linear-log bucketing” for this post! I also used it twice last week, in otherwise unrelated contexts, so I figure it deserves more publicity.

I’m sure the idea is old, but I first came across this strategy in jemalloc’s binning scheme for allocation sizes. The general idea is to simplify allocation and reduce external fragmentation by rounding allocations up to one of a few bin sizes. The simplest scheme would round up to the next power of two, but experience shows that’s extremely wasteful: in the worst case, an allocation for $$k$$ bytes can be rounded up to $$2k - 2$$ bytes, for almost 100% space overhead! Jemalloc further divides each power-of-two range into 4 bins, reducing the worst-case space overhead to 25%.

This sub-power-of-two binning covers medium and large allocations. We still have to deal with small ones: the ABI forces alignment on every allocation, regardless of their size, and we don’t want to have too many small bins (e.g., 1 byte, 2 bytes, 3 bytes, …, 8 bytes). Jemalloc adds another constraint: bins are always multiples of the allocation quantum (usually 16 bytes).

The sequence for bin sizes thus looks like: 16, 32, 48, 64, 80, 96, 112, 128, 160, 192, 224, 256, 320, 384, … (0 is special because malloc must either return NULL [bad for error checking] or treat it as a full blown allocation).

I like to think of this sequence as a special initial range with 4 linearly spaced subbins (0 to 63), followed by power-of-two ranges that are again split in 4 subbins (i.e., almost logarithmic binning). There are thus two parameters: the size of the initial linear range, and the number of subbins per range. We’re working with integers, so we also know that the linear range is at least as large as the number of subbins (it’s hard to subdivide 8 integers in 16 bins).

Assuming both parameters are powers of two, we can find the bucket for any value with only a couple x86 instructions, and no conditional jump or lookup in memory. That’s a lot simpler than jemalloc’s implementation; if you’re into Java, HdrHistogram’s binning code is nearly identical to mine.

## Common Lisp: my favourite programmer’s calculator

As always when working with bits, I first doodled in SLIME/SBCL: CL’s bit manipulation functions are more expressive than C’s, and a REPL helps exploration.

Let linear be the $$\log\sb{2}$$ of the linear range, and subbin the $$\log\sb{2}$$ of the number of subbin per range, with linear >= subbin.

The key idea is that we can easily find the power of two range (with a BSR), and that we can determine the subbin in that range by shifting the value right to only keep its subbin most significant (nonzero) bits.

I clearly need something like $$\lfloor\log\sb{2} x\rfloor$$:

I’ll also want to treat values smaller than 2**linear as though they were about 2**linear in size. We’ll do that with

n-bits := (lb (logior x (ash 1 linear))) === (max linear (lb x))


We now want to shift away all but the top subbin bits of x

shift := (- n-bits subbin)
sub-index := (ash x (- shift))


For a memory allocator, the problem is that the last rightward shift rounds down! Let’s add a small mask to round things up:

mask := (ldb (byte shift 0) -1) ; that's shift 1 bits
sub-index := (ash rounded (- shift))


We have the top subbin bits (after rounding) in sub-index. We only need to find the range index

range := (- n-bits linear) ; n-bits >= linear


Finally, we combine these two together by shifting index by subbin bits

index := (+ (ash range subbin) sub-index)


Extra! Extra! We can also find the maximum value for the bin with

size := (logandc2 rounded mask)


Assembling all this yields

Let’s look at what happens when we want $$2\sp{2} = 4$$ subbin per range, and a linear progression over $$[0, 2\sp{4} = 16)$$.

CL-USER> (bucket 0 4 2)
0 ; 0 gets bucket 0 and rounds up to 0
0
CL-USER> (bucket 1 4 2)
1 ; 1 gets bucket 1 and rounds up to 4
4
CL-USER> (bucket 4 4 2)
1 ; so does 4
4
CL-USER> (bucket 5 4 2)
2 ; 5 gets the next bucket
8
CL-USER> (bucket 9 4 2)
3
12
CL-USER> (bucket 15 4 2)
4
16
CL-USER> (bucket 17 4 2)
5
20
CL-USER> (bucket 34 4 2)
9
40


The sequence is exactly what we want: 0, 4, 8, 12, 16, 20, 24, 28, 32, 40, 48, …!

The function is marginally simpler if we can round down instead of up.

CL-USER> (bucket-down 0 4 2)
0 ; 0 still gets the 0th bucket
0 ; and rounds down to 0
CL-USER> (bucket-down 1 4 2)
0 ; but now so does 1
0
CL-USER> (bucket-down 3 4 2)
0 ; and 3
0
CL-USER> (bucket-down 4 4 2)
1 ; 4 gets its bucket
4
CL-USER> (bucket-down 7 4 2)
1 ; and 7 shares it
4
CL-USER> (bucket-down 15 4 2)
3 ; 15 gets the 3rd bucket for [12, 15]
12
CL-USER> (bucket-down 16 4 2)
4
16
CL-USER> (bucket-down 17 4 2)
4
16
CL-USER> (bucket-down 34 4 2)
8
32


That’s the same sequence of bucket sizes, but rounded down in size instead of up.

## What’s it good for?

I first implementated this code to mimic’s jemalloc binning scheme: in a memory allocator, a linear-logarithmic sequence give us alignment and bounded space overhead (bounded internal fragmentation), while keeping the number of size classes down (controlling external fragmentation).

High dynamic range histograms use the same class of sequences to bound the relative error introduced by binning, even when recording latencies that vary between microseconds and hours.

I’m currently considering this binning strategy to handle a large number of timeout events, when an exact priority queue is overkill. A timer wheel would work, but tuning memory usage is annoying. Instead of going for a hashed or hierarchical timer wheel, I’m thinking of binning events by timeout, with one FIFO per bin: events may be late, but never by more than, e.g., 10% their timeout. I also don’t really care about sub millisecond precision, but wish to treat zero specially; that’s all taken care of by the “round up” linear-log binning code.

In general, if you ever think to yourself that dispatching on the bitwidth of a number would mostly work, except that you need more granularity for large values, and perhaps less for small ones, linear-logarithmic binning sequences may be useful. They let you tune the granularity at both ends, and we know how to round values and map them to bins with simple functions that compile to fast and compact code!

P.S. If a chip out there has fast int->FP conversion and slow bit scans(!?), there’s another approach: convert the integer to FP, scale by, e.g., $$1.0 / 16$$, add 1, and shift/mask to extract the bottom of the exponent and the top of the significand. That’s not slow, but unlikely to be faster than a bit scan and a couple shifts/masks.