This project contains a C++11 implementation of Equihash problem solver, particularly its "200,9" variant as currently used by Zcash. In addition, there is a python binding for using the solver from python.
For building options and target support, please see "Build" section. Currently, only Linux and x86-64 is supported (windows port is in progress).
An intention of this README is to describe algorithmic and implementation approach of the contained solver and to help a reader to understand the code better. It expects that the user is already familiar with Equihash problem (generalized birthday paradox problem).
Current performance of the solver is >7 solutions/s on Intel(R) Core(TM) i5-4670K CPU @ 3.40GHz, which translates to roughly 255ms and average of 1.87 solutions for algorithm iteration (one nonce).
(For binary builds, please go to https://github.com/morpav/zceq_solver--bin).
(Document in progres..)
- Algorithm outline
- Used terminology
- Design discussion and optimizations
- Memory management
- Main solver
- General optimization
- Future work and enhancements
The solver works best when compiled with modern C++ compiler, particularly Clang v3.9.
The easies way how to build the solver is within a docker container, as provided by this project. Simply run
The target directory for the build is build-final.
The code has been compiled only on Linux by GCC and Clang compilers. We don't expact any particular issues with porting to windows, but no work has been done here. There is no external dependecy other then C++ standard library in the code.
The provided makefile expects clang++ installed. It builds an executable with profiling enabled, runs a short benchmark and then builds final executable and shared library (in build-final).
make NOPROFILING=1 to skip the profiling build. You can switch
to gcc then. (Profiling using gcc could be done too).
Alternatively, the software can be build with scons.
The scons build also supports cross building for windows (profiling is
automatically disabled for this scenario). See
scons -h for all
String = A sequence of bits as produced by Blake2 hash function and conforming to equihash definition. During the solution process, strings are reduced in length when collisions are detected.
Segment = A part of string subject to finding collisions in one step of the algorithm. Particularly, 200,9 variant of the problem has 10 segments, 20bits each.
(Algorithm) iteration = Process of finding solutions for one input (a block header and a fixed nonce).
Step = Part of iteration which finds collisions of one string segment and produces strings one segment shorter. The last step produces solutions, not other strings (there is nothing to collide).
(Collision) Pair = Two strings with the same first segment's value, later XOR-ed together to produce a new string for the next algorithm step.
Pair link = Small object containing indices of two strings which were source strings from step i for some string in step i+1.
Pair index = (or only index) Data structure holding information about which two source strings produced which output string.
Bucket = A collection of strings with similar first segment (the same bit prefix of the segment). The main property of a bucket is that string collisions can happen only among strings from the same bucket. Buckets allow decomposing the problem and solve it per partes. A size of buckets (or bit length) can be changed in alg. configuration, but 8bit prefix seems to work the best.
Collision group = A set of strings (from the same bucket) with the same first hash segment.
Basically, the algorithm is a variant on a classical hashtable-based collision finding. Since every problem is different the algorithm is designed to fit the problem as closely as possible.
Some implementation aspects of the algorithm are discussed separately later. Here are the main steps:
At first, 2^21 strings is generated and distributed into buckets. Within buckets, the strings are stored sequentially in memory. Each string has its own unique number
j. These numbers form a solution if the string is later a part of solution. The number j is physically stored in the string object itself, not in any separate data structure. At first. It shares space with future pair link (l0, l1), placed into the string as well. Initial string number can be considered as a trivial link with only one virtual predecessor.
Perform one step of collision searching. Particularly, for each bucket in increasing order:
2.1) We linearly scan strings in the bucket and compute a histogram of first segment values. So we count number of occurrences of every possible first segment's value. First few bits of the segment value are the same for all stings in the segment, so they are ignored.
For this counting, we have an array Counts with number of items equal to number of possible different segment values. When we load a string, we increase a number in a particular field of the array.
2.1) During the scan, we store linearly the first segment's value into another array TmpSeg for later usage. Indices of the array are the same as indices of the source strings. Also we read a link field of the string in hand and copy the link value into and separate index data structure, again indices of strings and links correspond.
2.3) We iterate through the histogram and eliminate all segment values (collision groups) with number of strings smaller then 2 or higher then 13 (configurable). These strings cannot typically produce a reasonable solution. It is obvious for cases <2, there cannot be a collision. For the higher sizes, the insight is following. Input data should not be distinguishable from random data (given that blake2b is a good hash function). It is highly improbable that more then 13 strings collide in a particular segment. But it happens in reality quite often when duplicate strings are involved in in some colliding strings (transitively). Filtering of collision groups greater then 13 tries to filter invalid solutions early. It is a really cheap check as well.
While eliminating collision groups of bad sizes, we calculate cumulative sum of number of strings participating in a not-eliminated collision groups. At the and, each collision group (segment value) has a continuous range of indices in an output array Collisions. Ranges of different collision groups are of course non-overlapping.
2.4) We go through array TmpSeg (with stored first segment values) and if a segment value has not been eliminated in the previous step, index of the value(string) is stored into array Collisions (into the precomputed position). After processing complete TmpSeg array, we have indices of all colliding strings sitting next to each other in one array. We know where exactly indices for a particular collision group are and how big the group is.
2.5) We take collision groups one by one and produce output strings (each pair of strings produces an output string). Here, we know that the groups are limited in size and complete. We generate all output strings (string for the next step) together.
While generating new strings, we reduce one segment and write the strings into particular output bucket, based on the now new first segment. We store indices of the source strings into a pair link field within the producing string. This link field will be copied into standalone index data structure in the next step, while computing then next histogram.
2.6) When all buckets are processed, we can forget the input strings (start to reuse the occupied memory) and find collisions for the next semgent in the strings.
If 8 segments has not been reduced yet, continue with step 2).
When collisions for 9th segment are found by the same approach already described, we do not produce any other strings, but we use information stored in the pair link objects to track down the source strings. The translation is pretty straightforward. The output is a list of 512 indices.
If the list contains any duplicate items, the solution candidate is rejected. Otherwise indices are reorder to conform to zcash algorithm binding and a solution is produced.
Design discussion and implementation
It can seem that this algorithm does too much unnecessary work. There are some explanations of this approach.
Design discussion and implementation
In general, to produce a fast solver one has to make a lot of design decisions. There is a strong tension between solution generality and exploiting every possible information to be faster.
We tried to go the second way with allowing the code to be general in cases when a modern compiler can reduce a general case to efficient particular case in compile time.
There are two main logical parts of the solver. A strings generator, producing blake2b hashes and collision search part. An algorithm of the first is exactly defined and the only space for efficiency is reduction of unnecessary work and efficient usage of CPU power.
Initial string generation (blake2b execution) takes roughly 30%-40% of time spent in solving equihash problem (after all following optimizations). It is surprisingly large part of the whole computation. It is worth to make the hash computation as fast as possible.
Excessive calls to 'compress'.
When one tries to use a reference implementation of blake2b then the s
The single most computationally intensive part of blake2b hash function is so called 'compress' function which updates an internal hash state by a block of input data of size 128B. This if performed for each input block one by one. Because the input size for zcash equihash is 140B of block header + 4B to make generated strings unique, 2 calls to 'compress' function are needed.
The first block (128B) is the same for all 2^N hash computation. We can reuse the internal state and call 'compress' only once per output hash.
Reference implementation of blake2b hash function does call compress twice per hash result even when the first block is provided at the beginning only once for all output strings. Not necessary data copying is performed too and it makes the computation even slower. This is independent of used ISA specialization.
schopny odhodlany sebevedomy
Too complex 'compress' function for second input block.
The second block of input function is mostly empty - only first 16 bytes is non-empty (144B - 128B) and the rest is by definition set to 0. By using this fact we can simplify implementation of the compress function because we eliminate loading and adding of most of the input data.
Similarly, we do not need all result bytes (only 50 from 64) so we can eliminate further computation.
Algorithmically inefficient compress in zcash context
All common implementations of blake2b hash function are constructed for computing single hash output for single input data. Even when some implementation is specialized e.g. for vector instruction set to speedup the computation, the dataflow of the algorithm limits gains given by the vectorization.
In context of zcash we need to run the hash function 2^20-times. So we can choose different vectorization strategy to explit this fact. The basic idea is to use vector instructions to compute more then one hash function at the same time. E.g. AVX2 instructions operate on 4 64bit integer values at once so we can compute 4 hashes. A critical property is that all 4 hashes and input data are independent so the computations needn't to share any data.
Other nice property is that a structure of the code is the same as for basic scalar implementation. The only difference is that a vector instruction computes with more data.
- Compile time evaluation and type derivation.
- Data structures selection (sizes, motivation), alignment.
- Memory allocation (space allocator).
Explain low-level optimization techniques applied, how and why the code is shaped becasuse of this:
- Cache locality
- Exploit HW prefetching
- SW prefetching
- Non-temporal writes to indices.
- Manual loop unrolling or other "forcing" techniques for compilers.
- Branch-less code
- Profile guided optimization
- (in makefile, but can be distributed with source code for users to compile)
- Huge pages - The solver tries to allocate its data using mmap and to obtain huge pages
- Reduce segment prefix already defined by bucket, where the string is stored.
- Take bucket bits not from the beginning.
Since the strings must be collided in prescribed way and the strings are treated as big endian, some extra work is needed for efficient implementation.
We want to read the first segment of a string really fast (on little endian machines) because it is a frequent operation in a hot loop. So we preprocess the strings to reorder bits in the produced hashes. The final form is so that only single load is needed with possibly one bitwise shift.
The reorder is done only once, during initial strings generation, particularly lower and higher 4 bits are swapped in every 3rd byte in every 5 bytes. The exact representation as in original solver is not needed, only each segment must contain the same bits (even reordered) as specified by "algorithm binding".
There is an option of the solver to produce "expanded" version of the strings so that each segment occupies exactly 3 bytes (not only 20bits). This eliminates additional shift operation otherwise needed when accessing the first segment every second algorithm step. It turns out that it the additional memory causes the algorithm to run slower (it is not so surprising).
The reorder is done during copying the strings to a final position to eliminate copying. 32B is processes instead of 25B to allow compiler to use vector instruction on modern architectures.
- Inefficient interface (python better - holds solver instance)
- Diff. implementation for solution validation and main solving.
It can be beneficial to switch to simpler algorithm for later steps. Some measurements show that when the strings
There is a plenty of parameters (zceq_config.h) which can be tweaked for better general performance. It is easy to find best ones for one machine or maybe CPU, but it is usually not the wanted result.
Currently, the code performs best on AVX2 CPUs, built target "native". But it can be run on any platform capable of building the code (x86-64 considered mainly).
There is a run time detection of the CPU features and different code runs for AVX2, AVX, etc. Better support for older architectures can be added.
There are three different implmentations of the hash function for AVX2, one from sodium library (not competitive in solving mode, great in checking mode), our original implementation using intrinsics and lately, we integrated xenon's great ASM implementation when its C binding is finally available. Thanks to this integration AVX1 machines can benefit from fast blake2b as well.
The fact that intrinsic implementation for AVX2 is quite competitive performance-wise to manually written asm code is promising. Very similar (vectorized/batch) implementation can be done for generic SSE2 (or better SSSE3) CPUs. The amount of code for the intrinsics-based AVX2 batch implementation is roughly 150 lines of code. Roughly the same code can be written for older CPUs to still benefit from independent vectorization of 2 blake2b.
Future work, potential
The project is clearly just after rush development/research mode. The code needs to be properly cleaned up and documented. Besides theese engineering tasks, there is a few of options how to make it run even faster. We didn't have a time to try all options.
The final step of the alg. is not optimal. For example, it would be probably beneficial to detect and store all candidate solutions during last collisions finding and then start to validate them if they are really solutions or not. Currently, we check every candidate immediately and it must negatively interact with the just running collision search. It is mostly cache issue.
We can not store first segment's prefix (already defined by bucket in which the string is located) so that the strings could be smaller in memory.
The solution extraction (translation of pair link into a solution) and validation itself can be improved as well. Currently we find duplicates by sorting all 512 indices and linearly looking for duplicates.
E.g. if we produce all candidate solution and then find duplicates at one moment by a kind of hash/lookup table (for example by) it can be done pretty efficiently. We can use some large lookup table (few kB) because we would not risk bad L1 cache interacions with other running tasks.
Better testing and tweaking of the algorithm to produce consistent result across different CPUs (and memory subsystems).
Memory consumption can be reduced. Not all memory blocks are currently managed by space space allocator (manually).
Better build process, port to Windows.
The biggest influancer for this solver has been xenon's asm solver. Besides the obvious reuse of his asm code, we reused an idea of encoding two bits of informaion into a position of the strings (allowed by two level bucketing or partitioning). It is really a great idea that allows to stay on 32bits per pair link instance!
There is no threading support added. The solver can be easily executed from more threads in the same process or even from different processes. The reason why we didn't spend time with threads is that we cannot see an obvious benefit from solving the same problem instance by more threads at the same time. It seems quite sure (a guess based on quite a few hours with the algorithms and implementation) that it is significatnly more efficient (and easier) to run independent solving processes (different nonces). Since one iteration takes ~300ms on modern hardware, we don't see it as a latency/timing issue.
The python binding is pretty new so there can be bugs there. Obvious benefit of the python binding in comparin with CLI inteface is that it can hold a state, so the solver can by reused for a lot of computations.
ZCash Open Source Miner Challenge interface -