I have a bunch of vectors (~500). I need to find triple products of all the combinations of the vectors in OpenCL. There are plenty of combination algorithms (r out of n things) in C++ but I am yet to find any implemented for GPU. I have seen quite a few parallel permutation algorithms in Cuda but I just want to know if there are any viable combination algorithms present?
I'll need to guess a bit here and there to answer your question.
I suppose you have an array V of n (~500) vectors. These vectors are all of same dimensionality m (probably m=3).
What you want is the component wise product of each 3 vectors vi, vj, vk where i,j,k in {0,..,n-1}.
Simple 3-dimensional example:
result[idx].x = V[i].x * V[j].x * V[k].x;
result[idx].y = V[i].y * V[j].y * V[k].y;
result[idx].z = V[i].z * V[j].z * V[k].z;
Now maybe your vectors are not 3-dimensional and maybe you don't want the component wise product but the sum of it (like in dot product), but I'm sure you're able to djust the code accordingly.
The real question here is how to compute all possible i,j,k and idx. Correct?
Now with CUDA you are in a very fortunate position. You can just launch n*n*n threads in a grid and therefore get i,j,k for free without having to think about ways to compute combinations or permutations at all. Just do the following:
dim3 grid, block;
block.x = n;
block.y = 1;
block z = 1;
grid.x = n;
grid.y = n;
grid.z = 1;
compute_product_kernel<<<grid, block>>>( V, result );
This way you'll launch n*n blocks of n threads. Computing i,j,k becomes trivial, computing idx is easy:
__device__ void compute_product_kernel( myVector* V, myVector* result)
{
int i = blockIdx.x;
int j = blockIdx.y;
int k = threadIdx.x;
int idx = i * gridDim.y * blockDim.x + j * blockDim.x + k;
...
}
Of course all of this only works because your n is within the limits of CUDA's block and grid range.
Two more things though:
Maybe you want permutations instead of combinations. You could do that by skipping every combination where any two of i,j,k are the same. But I'd recommend keeping them anyway because computing when to skip is probably more expensive that doing the actual work. Also I'd advise against using the permutation to save memory for result because it would save you less that 1% and make the calculation much more complex.
Are you sure you've got enough memory to actually do this? Storing the result requires n*n*n*m*sizeof(float) bytes. With n=500 and m=3 that would already be 1.5 GB. Is that really what you are looking for? Maybe the next step of your processing can be combined into the calculation so that storing the intermediate result is not neccessary.
I have been looking at this lib Random123 and associated quote:
One mysterious man came to my booth and asked what I knew about generating random numbers with OpenCL. I told him about implementations of the Mersenne Twister, but he wasn't impressed. He told me about a new technical paper that explains how to generate random numbers on GPUs by combining integer counters and block ciphers. In reverential tones, he said that counter-based random number generators (CBRNGs) produce numbers with greater statistical randomness than the MT and with much greater speed.
I was able to get a demo running using this kernel:
__kernel void counthits(unsigned n, __global uint2 *hitsp) {
unsigned tid = get_global_id(0);
unsigned hits = 0, tries = 0;
threefry4x32_key_t k = {{tid, 0xdecafbad, 0xfacebead, 0x12345678}};
threefry4x32_ctr_t c = {{0, 0xf00dcafe, 0xdeadbeef, 0xbeeff00d}};
while (tries < n) {
union {
threefry4x32_ctr_t c;
int4 i;
} u;
c.v[0]++;
u.c = threefry4x32(c, k);
long x1 = u.i.x, y1 = u.i.y;
long x2 = u.i.z, y2 = u.i.w;
if ((x1*x1 + y1*y1) < (1L<<62)) {
hits++;
}
tries++;
if ((x2*x2 + y2*y2) < (1L<<62)) {
hits++;
}
tries++;
}
hitsp[tid].x = hits;
hitsp[tid].y = tries;
}
My questions are now, will this not generate the same random numbers every time its run, a random number is based on the global id ? How can I generate new random numbers each time. Possible to provide a seed as a parameter for the kernel and then use that somehow?
Anyone who have been using this lib and can give me some more insight in the use of it?
Yes. The example code generates the same sequences of random numbers every time it is called.
To get different streams of random numbers, just initialize any of the values k[1..3] and/or c[1..3] differently. You can initialize them from command line arguments, environment variables, time-of-day, saved state, /dev/urandom, or any other source. Just be aware that:
a) if you initialize all of them exactly the same way in two different runs, then those two runs will get the same stream of random numbers
b) if you initialize them differently in two different runs, then those two runs will get different streams of random numbers.
Sometimes you want property a). Sometimes you want property b). Take a moment to think about which you want and be sure that you're doing what you intend.
More generally, the functions in the library, e.g., threefry4x32, have no state. If you change any bit in the input (i.e., any bit in any of the elements of c or k), you'll get a completely different random, statistically independent, uniformly distributed output.
P.S. I'm one of the authors of the library and the paper "Parallel Numbers: As Easy as 1, 2, 3":
http://dl.acm.org/citation.cfm?id=2063405
If you're not a subscriber to the ACM digital library, the link above may hit a pay-wall. Alternatively, you can obtain the paper free of charge by following the link on this page:
http://www.thesalmons.org/john/random123/index.html
I can't help you with the library per se, but I can tell you that the most common way to generate random numbers in OpenCL is to save some state between calls to the kernel.
Random number generators usually use a state, from which a new state and a random number are generated. In practice, this isn't complicated at all: you just pass an extra array that holds state. In my codes, I implement random numbers as follows:
uint rand_uint(uint2* rvec) { //Adapted from http://cas.ee.ic.ac.uk/people/dt10/research/rngs-gpu-mwc64x.html
#define A 4294883355U
uint x=rvec->x, c=rvec->y; //Unpack the state
uint res = x ^ c; //Calculate the result
uint hi = mul_hi(x,A); //Step the RNG
x = x*A + c;
c = hi + (x<c);
*rvec = (uint2)(x,c); //Pack the state back up
return res; //Return the next result
#undef A
}
inline float rand_float(uint2* rvec) {
return (float)(rand_uint(rvec)) / (float)(0xFFFFFFFF);
}
__kernel void my_kernel(/*more arguments*/ __global uint2* randoms) {
int index = get_global_id(0);
uint2 rvec = randoms[index];
//Call rand_uint or rand_float a number of times with "rvec" as argument.
//These calls update "rvec" with new state, and return a random number
randoms[index] = rvec;
}
. . . then, all you do is pass an extra array that holds the RNG's state into random. In practice, you'll want to seed this array differently for each work item.
0xdecafbad, 0xfacebead, 0x12345678 and 0xf00dcafe, 0xdeadbeef, 0xbeeff00d are just arbitrarily chosen numbers, they're not special. Any other number (even 0) could be used in their place -- I'll add a comment to the example code.
You can replace any of them with variables that you pass in; the only requirement for avoiding undesirable repetition in the output random "stream" is that you avoid repeating the (c, k) input tuple. The example code uses the thread id and loop index to ensure uniqueness, but you can easily add more variables to ensure uniqueness -- e.g. count the kernel invocations in the host code and pass that counter in, use that in place of one of the elements of k or c.
By the way, despite the name 'Counter-based random number generator', there's no requirement that the inputs (c, k) be 'counters', it's just that counters happen to be the most convenient idiom for ensuring that inputs don't repeat.
This is a long text. Please bear with me. Boiled down, the question is: Is there a workable in-place radix sort algorithm?
Preliminary
I've got a huge number of small fixed-length strings that only use the letters “A”, “C”, “G” and “T” (yes, you've guessed it: DNA) that I want to sort.
At the moment, I use std::sort which uses introsort in all common implementations of the STL. This works quite well. However, I'm convinced that radix sort fits my problem set perfectly and should work much better in practice.
Details
I've tested this assumption with a very naive implementation and for relatively small inputs (on the order of 10,000) this was true (well, at least more than twice as fast). However, runtime degrades abysmally when the problem size becomes larger (N > 5,000,000).
The reason is obvious: radix sort requires copying the whole data (more than once in my naive implementation, actually). This means that I've put ~ 4 GiB into my main memory which obviously kills performance. Even if it didn't, I can't afford to use this much memory since the problem sizes actually become even larger.
Use Cases
Ideally, this algorithm should work with any string length between 2 and 100, for DNA as well as DNA5 (which allows an additional wildcard character “N”), or even DNA with IUPAC ambiguity codes (resulting in 16 distinct values). However, I realize that all these cases cannot be covered, so I'm happy with any speed improvement I get. The code can decide dynamically which algorithm to dispatch to.
Research
Unfortunately, the Wikipedia article on radix sort is useless. The section about an in-place variant is complete rubbish. The NIST-DADS section on radix sort is next to nonexistent. There's a promising-sounding paper called Efficient Adaptive In-Place Radix Sorting which describes the algorithm “MSL”. Unfortunately, this paper, too, is disappointing.
In particular, there are the following things.
First, the algorithm contains several mistakes and leaves a lot unexplained. In particular, it doesn’t detail the recursion call (I simply assume that it increments or reduces some pointer to calculate the current shift and mask values). Also, it uses the functions dest_group and dest_address without giving definitions. I fail to see how to implement these efficiently (that is, in O(1); at least dest_address isn’t trivial).
Last but not least, the algorithm achieves in-place-ness by swapping array indices with elements inside the input array. This obviously only works on numerical arrays. I need to use it on strings. Of course, I could just screw strong typing and go ahead assuming that the memory will tolerate my storing an index where it doesn’t belong. But this only works as long as I can squeeze my strings into 32 bits of memory (assuming 32 bit integers). That's only 16 characters (let's ignore for the moment that 16 > log(5,000,000)).
Another paper by one of the authors gives no accurate description at all, but it gives MSL’s runtime as sub-linear which is flat out wrong.
To recap: Is there any hope of finding a working reference implementation or at least a good pseudocode/description of a working in-place radix sort that works on DNA strings?
Well, here's a simple implementation of an MSD radix sort for DNA. It's written in D because that's the language that I use most and therefore am least likely to make silly mistakes in, but it could easily be translated to some other language. It's in-place but requires 2 * seq.length passes through the array.
void radixSort(string[] seqs, size_t base = 0) {
if(seqs.length == 0)
return;
size_t TPos = seqs.length, APos = 0;
size_t i = 0;
while(i < TPos) {
if(seqs[i][base] == 'A') {
swap(seqs[i], seqs[APos++]);
i++;
}
else if(seqs[i][base] == 'T') {
swap(seqs[i], seqs[--TPos]);
} else i++;
}
i = APos;
size_t CPos = APos;
while(i < TPos) {
if(seqs[i][base] == 'C') {
swap(seqs[i], seqs[CPos++]);
}
i++;
}
if(base < seqs[0].length - 1) {
radixSort(seqs[0..APos], base + 1);
radixSort(seqs[APos..CPos], base + 1);
radixSort(seqs[CPos..TPos], base + 1);
radixSort(seqs[TPos..seqs.length], base + 1);
}
}
Obviously, this is kind of specific to DNA, as opposed to being general, but it should be fast.
Edit:
I got curious whether this code actually works, so I tested/debugged it while waiting for my own bioinformatics code to run. The version above now is actually tested and works. For 10 million sequences of 5 bases each, it's about 3x faster than an optimized introsort.
I've never seen an in-place radix sort, and from the nature of the radix-sort I doubt that it is much faster than a out of place sort as long as the temporary array fits into memory.
Reason:
The sorting does a linear read on the input array, but all writes will be nearly random. From a certain N upwards this boils down to a cache miss per write. This cache miss is what slows down your algorithm. If it's in place or not will not change this effect.
I know that this will not answer your question directly, but if sorting is a bottleneck you may want to have a look at near sorting algorithms as a preprocessing step (the wiki-page on the soft-heap may get you started).
That could give a very nice cache locality boost. A text-book out-of-place radix sort will then perform better. The writes will still be nearly random but at least they will cluster around the same chunks of memory and as such increase the cache hit ratio.
I have no idea if it works out in practice though.
Btw: If you're dealing with DNA strings only: You can compress a char into two bits and pack your data quite a lot. This will cut down the memory requirement by factor four over a naiive representation. Addressing becomes more complex, but the ALU of your CPU has lots of time to spend during all the cache-misses anyway.
You can certainly drop the memory requirements by encoding the sequence in bits.
You are looking at permutations so, for length 2, with "ACGT" that's 16 states, or 4 bits.
For length 3, that's 64 states, which can be encoded in 6 bits. So it looks like 2 bits for each letter in the sequence, or about 32 bits for 16 characters like you said.
If there is a way to reduce the number of valid 'words', further compression may be possible.
So for sequences of length 3, one could create 64 buckets, maybe sized uint32, or uint64.
Initialize them to zero.
Iterate through your very very large list of 3 char sequences, and encode them as above.
Use this as a subscript, and increment that bucket.
Repeat this until all of your sequences have been processed.
Next, regenerate your list.
Iterate through the 64 buckets in order, for the count found in that bucket, generate that many instances of the sequence represented by that bucket.
when all of the buckets have been iterated, you have your sorted array.
A sequence of 4, adds 2 bits, so there would be 256 buckets.
A sequence of 5, adds 2 bits, so there would be 1024 buckets.
At some point the number of buckets will approach your limits.
If you read the sequences from a file, instead of keeping them in memory, more memory would be available for buckets.
I think this would be faster than doing the sort in situ as the buckets are likely to fit within your working set.
Here is a hack that shows the technique
#include <iostream>
#include <iomanip>
#include <math.h>
using namespace std;
const int width = 3;
const int bucketCount = exp(width * log(4)) + 1;
int *bucket = NULL;
const char charMap[4] = {'A', 'C', 'G', 'T'};
void setup
(
void
)
{
bucket = new int[bucketCount];
memset(bucket, '\0', bucketCount * sizeof(bucket[0]));
}
void teardown
(
void
)
{
delete[] bucket;
}
void show
(
int encoded
)
{
int z;
int y;
int j;
for (z = width - 1; z >= 0; z--)
{
int n = 1;
for (y = 0; y < z; y++)
n *= 4;
j = encoded % n;
encoded -= j;
encoded /= n;
cout << charMap[encoded];
encoded = j;
}
cout << endl;
}
int main(void)
{
// Sort this sequence
const char *testSequence = "CAGCCCAAAGGGTTTAGACTTGGTGCGCAGCAGTTAAGATTGTTT";
size_t testSequenceLength = strlen(testSequence);
setup();
// load the sequences into the buckets
size_t z;
for (z = 0; z < testSequenceLength; z += width)
{
int encoding = 0;
size_t y;
for (y = 0; y < width; y++)
{
encoding *= 4;
switch (*(testSequence + z + y))
{
case 'A' : encoding += 0; break;
case 'C' : encoding += 1; break;
case 'G' : encoding += 2; break;
case 'T' : encoding += 3; break;
default : abort();
};
}
bucket[encoding]++;
}
/* show the sorted sequences */
for (z = 0; z < bucketCount; z++)
{
while (bucket[z] > 0)
{
show(z);
bucket[z]--;
}
}
teardown();
return 0;
}
If your data set is so big, then I would think that a disk-based buffer approach would be best:
sort(List<string> elements, int prefix)
if (elements.Count < THRESHOLD)
return InMemoryRadixSort(elements, prefix)
else
return DiskBackedRadixSort(elements, prefix)
DiskBackedRadixSort(elements, prefix)
DiskBackedBuffer<string>[] buckets
foreach (element in elements)
buckets[element.MSB(prefix)].Add(element);
List<string> ret
foreach (bucket in buckets)
ret.Add(sort(bucket, prefix + 1))
return ret
I would also experiment grouping into a larger number of buckets, for instance, if your string was:
GATTACA
the first MSB call would return the bucket for GATT (256 total buckets), that way you make fewer branches of the disk based buffer. This may or may not improve performance, so experiment with it.
I'm going to go out on a limb and suggest you switch to a heap/heapsort implementation. This suggestion comes with some assumptions:
You control the reading of the data
You can do something meaningful with the sorted data as soon as you 'start' getting it sorted.
The beauty of the heap/heap-sort is that you can build the heap while you read the data, and you can start getting results the moment you have built the heap.
Let's step back. If you are so fortunate that you can read the data asynchronously (that is, you can post some kind of read request and be notified when some data is ready), and then you can build a chunk of the heap while you are waiting for the next chunk of data to come in - even from disk. Often, this approach can bury most of the cost of half of your sorting behind the time spent getting the data.
Once you have the data read, the first element is already available. Depending on where you are sending the data, this can be great. If you are sending it to another asynchronous reader, or some parallel 'event' model, or UI, you can send chunks and chunks as you go.
That said - if you have no control over how the data is read, and it is read synchronously, and you have no use for the sorted data until it is entirely written out - ignore all this. :(
See the Wikipedia articles:
Heapsort
Binary heap
"Radix sorting with no extra space" is a paper addressing your problem.
Performance-wise you might want to look at a more general string-comparison sorting algorithms.
Currently you wind up touching every element of every string, but you can do better!
In particular, a burst sort is a very good fit for this case. As a bonus, since burstsort is based on tries, it works ridiculously well for the small alphabet sizes used in DNA/RNA, since you don't need to build any sort of ternary search node, hash or other trie node compression scheme into the trie implementation. The tries may be useful for your suffix-array-like final goal as well.
A decent general purpose implementation of burstsort is available on source forge at http://sourceforge.net/projects/burstsort/ - but it is not in-place.
For comparison purposes, The C-burstsort implementation covered at http://www.cs.mu.oz.au/~rsinha/papers/SinhaRingZobel-2006.pdf benchmarks 4-5x faster than quicksort and radix sorts for some typical workloads.
You'll want to take a look at Large-scale Genome Sequence Processing by Drs. Kasahara and Morishita.
Strings comprised of the four nucleotide letters A, C, G, and T can be specially encoded into Integers for much faster processing. Radix sort is among many algorithms discussed in the book; you should be able to adapt the accepted answer to this question and see a big performance improvement.
You might try using a trie. Sorting the data is simply iterating through the dataset and inserting it; the structure is naturally sorted, and you can think of it as similar to a B-Tree (except instead of making comparisons, you always use pointer indirections).
Caching behavior will favor all of the internal nodes, so you probably won't improve upon that; but you can fiddle with the branching factor of your trie as well (ensure that every node fits into a single cache line, allocate trie nodes similar to a heap, as a contiguous array that represents a level-order traversal). Since tries are also digital structures (O(k) insert/find/delete for elements of length k), you should have competitive performance to a radix sort.
I would burstsort a packed-bit representation of the strings. Burstsort is claimed to have much better locality than radix sorts, keeping the extra space usage down with burst tries in place of classical tries. The original paper has measurements.
It looks like you've solved the problem, but for the record, it appears that one version of a workable in-place radix sort is the "American Flag Sort". It's described here: Engineering Radix Sort. The general idea is to do 2 passes on each character - first count how many of each you have, so you can subdivide the input array into bins. Then go through again, swapping each element into the correct bin. Now recursively sort each bin on the next character position.
Radix-Sort is not cache conscious and is not the fastest sort algorithm for large sets.
You can look at:
ti7qsort. ti7qsort is the fastest sort for integers (can be used for small-fixed size strings).
Inline QSORT
String sorting
You can also use compression and encode each letter of your DNA into 2 bits before storing into the sort array.
dsimcha's MSB radix sort looks nice, but Nils gets closer to the heart of the problem with the observation that cache locality is what's killing you at large problem sizes.
I suggest a very simple approach:
Empirically estimate the largest size m for which a radix sort is efficient.
Read blocks of m elements at a time, radix sort them, and write them out (to a memory buffer if you have enough memory, but otherwise to file), until you exhaust your input.
Mergesort the resulting sorted blocks.
Mergesort is the most cache-friendly sorting algorithm I'm aware of: "Read the next item from either array A or B, then write an item to the output buffer." It runs efficiently on tape drives. It does require 2n space to sort n items, but my bet is that the much-improved cache locality you'll see will make that unimportant -- and if you were using a non-in-place radix sort, you needed that extra space anyway.
Please note finally that mergesort can be implemented without recursion, and in fact doing it this way makes clear the true linear memory access pattern.
First, think about the coding of your problem. Get rid of the strings, replace them by a binary representation. Use the first byte to indicate length+encoding. Alternatively, use a fixed length representation at a four-byte boundary. Then the radix sort becomes much easier. For a radix sort, the most important thing is to not have exception handling at the hot spot of the inner loop.
OK, I thought a bit more about the 4-nary problem. You want a solution like a Judy tree for this. The next solution can handle variable length strings; for fixed length just remove the length bits, that actually makes it easier.
Allocate blocks of 16 pointers. The least significant bit of the pointers can be reused, as your blocks will always be aligned. You might want a special storage allocator for it (breaking up large storage into smaller blocks). There are a number of different kinds of blocks:
Encoding with 7 length bits of variable-length strings. As they fill up, you replace them by:
Position encodes the next two characters, you have 16 pointers to the next blocks, ending with:
Bitmap encoding of the last three characters of a string.
For each kind of block, you need to store different information in the LSBs. As you have variable length strings you need to store end-of-string too, and the last kind of block can only be used for the longest strings. The 7 length bits should be replaced by less as you get deeper into the structure.
This provides you with a reasonably fast and very memory efficient storage of sorted strings. It will behave somewhat like a trie. To get this working, make sure to build enough unit tests. You want coverage of all block transitions. You want to start with only the second kind of block.
For even more performance, you might want to add different block types and a larger size of block. If the blocks are always the same size and large enough, you can use even fewer bits for the pointers. With a block size of 16 pointers, you already have a byte free in a 32-bit address space. Take a look at the Judy tree documentation for interesting block types. Basically, you add code and engineering time for a space (and runtime) trade-off
You probably want to start with a 256 wide direct radix for the first four characters. That provides a decent space/time tradeoff. In this implementation, you get much less memory overhead than with a simple trie; it is approximately three times smaller (I haven't measured). O(n) is no problem if the constant is low enough, as you noticed when comparing with the O(n log n) quicksort.
Are you interested in handling doubles? With short sequences, there are going to be. Adapting the blocks to handle counts is tricky, but it can be very space-efficient.
While the accepted answer perfectly answers the description of the problem, I've reached this place looking in vain for an algorithm to partition inline an array into N parts. I've written one myself, so here it is.
Warning: this is not a stable partitioning algorithm, so for multilevel partitioning, one must repartition each resulting partition instead of the whole array. The advantage is that it is inline.
The way it helps with the question posed is that you can repeatedly partition inline based on a letter of the string, then sort the partitions when they are small enough with the algorithm of your choice.
function partitionInPlace(input, partitionFunction, numPartitions, startIndex=0, endIndex=-1) {
if (endIndex===-1) endIndex=input.length;
const starts = Array.from({ length: numPartitions + 1 }, () => 0);
for (let i = startIndex; i < endIndex; i++) {
const val = input[i];
const partByte = partitionFunction(val);
starts[partByte]++;
}
let prev = startIndex;
for (let i = 0; i < numPartitions; i++) {
const p = prev;
prev += starts[i];
starts[i] = p;
}
const indexes = [...starts];
starts[numPartitions] = prev;
let bucket = 0;
while (bucket < numPartitions) {
const start = starts[bucket];
const end = starts[bucket + 1];
if (end - start < 1) {
bucket++;
continue;
}
let index = indexes[bucket];
if (index === end) {
bucket++;
continue;
}
let val = input[index];
let destBucket = partitionFunction(val);
if (destBucket === bucket) {
indexes[bucket] = index + 1;
continue;
}
let dest;
do {
dest = indexes[destBucket] - 1;
let destVal;
let destValBucket = destBucket;
while (destValBucket === destBucket) {
dest++;
destVal = input[dest];
destValBucket = partitionFunction(destVal);
}
input[dest] = val;
indexes[destBucket] = dest + 1;
val = destVal;
destBucket = destValBucket;
} while (dest !== index)
}
return starts;
}