# Overview This is a latency-optimized hashing framework based on Abseil's and others. At it's core it uses both a normal 64-bit multiply as well as a 64-bit multiply capturing both low and high 64-bit components of the result and XOR-ing them together. These are the primitives used in FxHash and Abseil respectively, although they both appear in others. The implementation has been *substantially* optimized for short inputs and latency over quality. As a result, this function does not remotely pass the SMHasher quality tests. However, basic collisions are rare, and I've included a small subset of the SMHasher collision testing directly to make sure the quality doesn't slip too far inadvertently. The customization framework is roughly similar to Abseil's and LLVM's but has been simplified significantly, inspired in some respects by the AHash API design and in others by my experience of all performance sensitive hashing implementations needing to work at a very low level to hit their performance targets. The abstractions are stripped down to facilitate this. # Details of the performance optimization This function is 2x - 4x faster than LLVM's on small inputs, and up to 2x faster than Abseil. Significant effort has gone into optimizing short strings in particular compared to Abseil. Small integer and pointer hashing is also faster than Abseil's by leveraging a lower quality 64-bit multiply in some cases inspired by FxHash. One consequence is that this routine is particulary fast for 32-bit integers. The short string improvements largely come from packing more of the bytes of string into as few multiplies as possible. While this fails to mix the bits sufficient to hit SMHasher's strict avalanche criteria and does leave some collision windows, it provides dramatic latency improvements. Some of these techniques come from Abseil's own bulk hashing routine but re-applied here. Others are novel, for example using small sizes to sample nicely uniform random data to efficiently handle the very small number of bits of data that need to be hashed. The other observed improvement is diligent handling of pairs and tuples and fairly aggressively turning things into integers. Some of the comparisons with Abseil aren't realistic as the Abseil hash table does some of these mappings before hashing. I've done this directly in the hash function as that seems cleaner. For long strings, the performance is comparable or a bit better than Abseil, and significantly better than LLVM's hash function. Overall, for short inputs this is hoped to be the fastest hash function that still gets "just enough" mixing for modern hash tables to perform well. # Details of the quality vs. latency tradeoff A key insight is that modern hash tables don't need especially high quality hash functions, but do benefit from something beyond the identify function. That isn't the target of SMHasher or other quality assessing tools and has resulted in unnecessarily aggressive hashing for any functions actually evaluated against it. Many hash functions turn off the high quality implementations evaluated with SMHasher for integer or pointer keys to recover latency & performance (AHash for example), but the same performance-oriented design applies beyond these narrow types, for example for short strings. However, a consequence is that there are serious limits to the quality of the hash function. The avalanche test is failed hilariously, etc., but in the exact same ways as Abseil itself fails it for integer keys. There are also real collisions spaces. For example, for 16-byte strings, there is one 64-bit value for the first 8 bytes that will have the same hash regardless of the other 8 bytes of the string. Some minor effort is taken to make this pattern unlikely to be a practical problem, but it is a clear theoretical weakness. It also means that this hash function couldn't be further from providing any hash-flooding DoS attack protection -- I expect it to be trivially easy to attack in this way by a motivated adversary. Defending against these attacks is defined as out-of-scope, in large part because even attempts that have made a compelling effort to address these issues such as HighwayHash have found serious limits. Instead, this takes a principled position that any such defense should be provided entirely at the data structure level with a strong worst-case bound rather than through strengthening the hash function. # Future work A subsequent PR will introduce a hash table inspired very heavily by the design of Abseil's "SwissTable" and using this hash function. The goal is to provide a significant improvement to hot hash tables such as the identifier table in the lexer of Carbon's toolchain. # Detailed benchmark data The benchmarks introduced are heavily inspired by the latency benchmarking of hash functions in Abseil. I've adapted them to fit better into Carbon's coding style and to try to have more stable results with broader coverage of types and string sizes. Running the benchmarks directly gives horizontal comparisons across different hash functions. That can be hard to read, so here is *just* the newly introduced hash function benchmark results on an AMD server: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 3.11ns ± 1% BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 3.11ns ± 1% BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 4.11ns ± 1% BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 3.12ns ± 1% BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 4.13ns ± 1% BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 3.16ns ± 2% BM_LatencyHash<RandValues<int*>, CarbonHashBench> 3.16ns ± 2% BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 4.03ns ± 2% BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 4.04ns ± 1% BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 4.34ns ± 2% BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 4.04ns ± 1% BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 4.34ns ± 2% BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 4.33ns ± 1% BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 4.33ns ± 1% BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 4.34ns ± 1% BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 4.33ns ± 1% BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 4.33ns ± 1% BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 1.95ns ± 4% BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 1.70ns ± 3% BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 3.52ns ± 3% BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 4.46ns ± 2% BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 7.69ns ± 1% BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 14.8ns ± 1% BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 21.5ns ± 1% BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 34.6ns ± 0% BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 63.1ns ± 1% BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 118ns ± 1% BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 225ns ± 1% ``` And on an ARM server: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 5.28ns ± 0% BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 5.29ns ± 0% BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 7.02ns ± 0% BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 5.34ns ± 1% BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 7.07ns ± 4% BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 5.36ns ± 2% BM_LatencyHash<RandValues<int*>, CarbonHashBench> 5.36ns ± 2% BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 7.19ns ± 3% BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 7.29ns ± 2% BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 7.31ns ± 4% BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 7.29ns ± 2% BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 7.31ns ± 4% BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 8.69ns ± 3% BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 8.69ns ± 3% BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 8.69ns ± 3% BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 8.69ns ± 3% BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 8.69ns ± 3% BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 2.64ns ± 2% BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 2.90ns ± 4% BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 6.14ns ± 1% BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 8.27ns ± 1% BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 13.8ns ± 0% BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 31.2ns ± 0% BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 49.9ns ± 0% BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 86.9ns ± 0% BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 163ns ± 0% BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 312ns ± 0% BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 610ns ± 0% ``` I don't have the same nice statistical multi-run error bars, but one run from my M1 MacBook: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 3.89 ns BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 3.87 ns BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 4.39 ns BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 3.93 ns BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 4.98 ns BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 3.87 ns BM_LatencyHash<RandValues<int*>, CarbonHashBench> 3.87 ns BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 4.86 ns BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 4.43 ns BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 4.41 ns BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 4.44 ns BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 4.69 ns BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 4.33 ns BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 4.38 ns BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 4.34 ns BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 4.35 ns BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 4.38 ns BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 1.15 ns BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 0.973 ns BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 3.03 ns BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 3.97 ns BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 6.64 ns BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 12.5 ns BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 17.9 ns BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 27.9 ns BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 48.1 ns BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 87.3 ns BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 166 ns ``` And here I have internally replaced the Carbon hash function with Abseil's hash function for "before" and then restored it in the "after" and computed the delta for each benchmark. This basically shows the speed-up (lower time -> lower latency -> speed-up -> good) over Abseil on an AMD server: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 4.00ns ± 1% 3.10ns ± 0% -22.45% (p=0.000 n=20+15) BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 4.01ns ± 1% 3.10ns ± 1% -22.64% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 6.25ns ± 1% 4.10ns ± 1% -34.30% (p=0.000 n=20+20) BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 4.02ns ± 1% 3.12ns ± 1% -22.50% (p=0.000 n=19+19) BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 6.25ns ± 1% 4.11ns ± 1% -34.20% (p=0.000 n=20+19) BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 4.03ns ± 1% 3.14ns ± 1% -22.17% (p=0.000 n=19+19) BM_LatencyHash<RandValues<int*>, CarbonHashBench> 5.95ns ± 1% 3.14ns ± 1% -47.24% (p=0.000 n=20+18) BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 6.04ns ± 1% 4.01ns ± 1% -33.64% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 5.96ns ± 1% 4.02ns ± 1% -32.51% (p=0.000 n=18+20) BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 5.93ns ± 1% 4.30ns ± 1% -27.56% (p=0.000 n=20+17) BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 7.97ns ± 1% 4.02ns ± 1% -49.50% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 7.98ns ± 1% 4.32ns ± 1% -45.88% (p=0.000 n=19+20) BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 4.40ns ± 2% 4.32ns ± 1% -1.81% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 5.94ns ± 1% 4.32ns ± 1% -27.25% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 10.0ns ± 1% 4.3ns ± 1% -56.56% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 8.04ns ± 1% 4.32ns ± 1% -46.29% (p=0.000 n=20+19) BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 7.95ns ± 1% 4.33ns ± 1% -45.59% (p=0.000 n=19+20) BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 3.28ns ± 3% 1.93ns ± 4% -41.19% (p=0.000 n=18+20) BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 3.05ns ± 3% 1.69ns ± 4% -44.52% (p=0.000 n=19+20) BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 5.88ns ± 2% 3.50ns ± 3% -40.42% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 8.92ns ± 1% 4.44ns ± 2% -50.22% (p=0.000 n=19+20) BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 12.0ns ± 1% 7.7ns ± 1% -36.16% (p=0.000 n=18+20) BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 18.8ns ± 0% 14.7ns ± 1% -21.73% (p=0.000 n=17+20) BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 25.5ns ± 1% 21.4ns ± 1% -16.18% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 38.7ns ± 2% 34.5ns ± 1% -10.78% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 69.7ns ± 1% 62.8ns ± 1% -9.88% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 130ns ± 1% 117ns ± 1% -9.45% (p=0.000 n=20+19) BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 244ns ± 0% 225ns ± 1% -8.11% (p=0.000 n=17+20) ``` ... and on an ARM server: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 6.48ns ± 1% 5.28ns ± 0% -18.62% (p=0.000 n=20+20) BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 7.40ns ± 1% 5.29ns ± 1% -28.45% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 10.4ns ± 0% 7.0ns ± 0% -32.34% (p=0.000 n=19+20) BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 6.56ns ± 1% 5.32ns ± 1% -18.95% (p=0.000 n=20+19) BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 10.8ns ± 2% 7.0ns ± 1% -34.89% (p=0.000 n=20+20) BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 6.71ns ± 3% 5.38ns ± 2% -19.84% (p=0.000 n=20+20) BM_LatencyHash<RandValues<int*>, CarbonHashBench> 10.3ns ± 3% 5.4ns ± 2% -47.67% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 10.9ns ± 2% 7.2ns ± 4% -33.67% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 10.7ns ± 4% 7.3ns ± 4% -31.66% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 10.5ns ± 3% 7.3ns ± 4% -30.71% (p=0.000 n=20+19) BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 14.1ns ± 3% 7.3ns ± 4% -48.32% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 14.0ns ± 1% 7.3ns ± 4% -47.95% (p=0.000 n=19+19) BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 9.41ns ± 4% 8.68ns ± 4% -7.71% (p=0.000 n=19+19) BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 12.2ns ± 2% 8.7ns ± 4% -28.81% (p=0.000 n=18+19) BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 18.9ns ± 2% 8.7ns ± 4% -54.17% (p=0.000 n=17+19) BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 15.6ns ± 2% 8.7ns ± 4% -44.37% (p=0.000 n=17+19) BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 15.5ns ± 2% 8.7ns ± 4% -44.08% (p=0.000 n=18+19) BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 5.89ns ± 2% 2.64ns ± 3% -55.26% (p=0.000 n=19+20) BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 5.73ns ± 3% 2.88ns ± 3% -49.71% (p=0.000 n=18+20) BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 10.1ns ± 1% 6.1ns ± 2% -39.00% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 15.7ns ± 0% 8.3ns ± 1% -47.27% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 21.2ns ± 0% 13.8ns ± 0% -34.81% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 37.9ns ± 0% 31.2ns ± 0% -17.77% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 56.8ns ± 0% 49.8ns ± 0% -12.21% (p=0.000 n=20+18) BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 93.8ns ± 0% 86.9ns ± 0% -7.38% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 174ns ± 0% 163ns ± 0% -6.03% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 330ns ± 0% 312ns ± 0% -5.25% (p=0.000 n=19+20) BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 641ns ± 0% 610ns ± 0% -4.79% (p=0.000 n=19+19) ``` This is the same as the above delta comparison, but with the "before" being LLVM's hash function: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 6.85ns ± 1% 3.10ns ± 1% -54.78% (p=0.000 n=20+19) BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 6.85ns ± 1% 3.10ns ± 1% -54.78% (p=0.000 n=20+19) BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 6.25ns ± 1% 4.09ns ± 1% -34.58% (p=0.000 n=20+20) BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 6.87ns ± 1% 3.12ns ± 2% -54.66% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 7.35ns ± 1% 4.10ns ± 1% -44.20% (p=0.000 n=20+19) BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 7.34ns ± 1% 3.13ns ± 1% -57.34% (p=0.000 n=20+18) BM_LatencyHash<RandValues<int*>, CarbonHashBench> 7.33ns ± 1% 3.13ns ± 2% -57.27% (p=0.000 n=20+18) BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 7.27ns ± 1% 3.99ns ± 1% -45.12% (p=0.000 n=20+18) BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 14.5ns ± 1% 4.0ns ± 1% -72.23% (p=0.000 n=19+19) BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 14.6ns ± 1% 4.3ns ± 2% -70.44% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 14.5ns ± 1% 4.0ns ± 1% -72.21% (p=0.000 n=20+19) BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 14.6ns ± 1% 4.3ns ± 1% -70.46% (p=0.000 n=20+18) BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 7.31ns ± 1% 4.33ns ± 1% -40.81% (p=0.000 n=18+20) BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 7.78ns ± 1% 4.32ns ± 1% -44.45% (p=0.000 n=18+20) BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 7.78ns ± 2% 4.33ns ± 1% -44.42% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 7.62ns ± 1% 4.32ns ± 1% -43.24% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 7.77ns ± 1% 4.33ns ± 1% -44.34% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 8.15ns ± 3% 1.94ns ± 5% -76.16% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 7.02ns ± 3% 1.69ns ± 4% -75.94% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 7.83ns ± 2% 3.50ns ± 3% -55.34% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 9.17ns ± 1% 4.43ns ± 2% -51.65% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 11.3ns ± 1% 7.6ns ± 1% -32.04% (p=0.000 n=20+19) BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 23.0ns ± 1% 14.7ns ± 1% -36.14% (p=0.000 n=20+19) BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 32.9ns ± 0% 21.4ns ± 1% -34.96% (p=0.000 n=17+19) BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 52.2ns ± 1% 34.4ns ± 1% -34.01% (p=0.000 n=19+18) BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 92.1ns ± 1% 62.8ns ± 1% -31.82% (p=0.000 n=19+19) BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 169ns ± 1% 117ns ± 1% -30.53% (p=0.000 n=20+19) BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 319ns ± 1% 224ns ± 1% -29.78% (p=0.000 n=20+18) ``` ... and on an ARM server: ``` BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench> 8.38ns ± 0% 5.27ns ± 0% -37.04% (p=0.000 n=20+20) BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> 8.39ns ± 1% 5.28ns ± 0% -37.01% (p=0.000 n=19+19) BM_LatencyHash<RandValues<std::pair<uint8_t, uint8_t>>, CarbonHashBench> 8.07ns ± 0% 7.02ns ± 0% -13.10% (p=0.000 n=19+20) BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> 8.48ns ± 1% 5.32ns ± 1% -37.25% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint16_t, uint16_t>>, CarbonHashBench> 9.34ns ± 2% 7.09ns ± 2% -24.14% (p=0.000 n=19+20) BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> 9.76ns ± 3% 5.37ns ± 2% -44.98% (p=0.000 n=20+20) BM_LatencyHash<RandValues<int*>, CarbonHashBench> 9.76ns ± 3% 5.37ns ± 2% -44.98% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> 10.1ns ± 2% 7.2ns ± 3% -29.36% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint64_t, uint32_t>>, CarbonHashBench> 11.9ns ± 2% 7.3ns ± 4% -38.68% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> 11.3ns ± 2% 7.3ns ± 4% -35.16% (p=0.000 n=19+19) BM_LatencyHash<RandValues<std::pair<int*, uint32_t>>, CarbonHashBench> 11.9ns ± 2% 7.3ns ± 4% -38.68% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench> 11.3ns ± 2% 7.3ns ± 4% -35.16% (p=0.000 n=19+19) BM_LatencyHash<RandValues<__uint128_t>, CarbonHashBench> 10.3ns ± 2% 8.7ns ± 3% -15.81% (p=0.000 n=19+20) BM_LatencyHash<RandValues<std::pair<uint64_t, uint64_t>>, CarbonHashBench> 11.6ns ± 3% 8.7ns ± 3% -25.44% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<int*, int*>>, CarbonHashBench> 11.6ns ± 3% 8.7ns ± 3% -25.44% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<uint64_t, int*>>, CarbonHashBench> 11.6ns ± 3% 8.7ns ± 3% -25.44% (p=0.000 n=20+20) BM_LatencyHash<RandValues<std::pair<int*, uint64_t>>, CarbonHashBench> 11.6ns ± 3% 8.7ns ± 3% -25.44% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 4>, CarbonHashBench> 9.39ns ± 2% 2.66ns ± 3% -71.66% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 8>, CarbonHashBench> 10.7ns ± 3% 2.9ns ± 3% -72.97% (p=0.000 n=19+18) BM_LatencyHash<RandStrings< true, 16>, CarbonHashBench> 11.8ns ± 1% 6.1ns ± 2% -47.75% (p=0.000 n=19+20) BM_LatencyHash<RandStrings< true, 32>, CarbonHashBench> 13.9ns ± 1% 8.3ns ± 1% -40.71% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 64>, CarbonHashBench> 16.8ns ± 1% 13.8ns ± 0% -17.83% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 256>, CarbonHashBench> 31.7ns ± 1% 31.2ns ± 0% -1.76% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 512>, CarbonHashBench> 43.5ns ± 0% 49.8ns ± 0% +14.56% (p=0.000 n=18+20) BM_LatencyHash<RandStrings< true, 1024>, CarbonHashBench> 66.2ns ± 0% 86.9ns ± 0% +31.39% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 2048>, CarbonHashBench> 112ns ± 0% 163ns ± 0% +46.09% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 4096>, CarbonHashBench> 201ns ± 0% 312ns ± 0% +55.49% (p=0.000 n=20+20) BM_LatencyHash<RandStrings< true, 8192>, CarbonHashBench> 379ns ± 0% 610ns ± 0% +61.08% (p=0.000 n=20+20) ``` Note that there is a significant regression on long strings compared to LLVM's hash function on the ARM server I have access to. This doesn't show up on the M1 at all, and is likely specific to inadequate throughput for the 64-bit multiply operations. This seems fine as a) our priority is for short strings, and b) the M1 and other ARM CPUs are likely to improve here over time given the prevalent use of this core technique. For example, Abseil's current hash algorithm has the same long-string behavior (and performance bottleneck) on this server. --------- Co-authored-by: josh11b <josh11b@users.noreply.github.com> Co-authored-by: Geoff Romer <gromer@google.com>
Carbon Language:
An experimental successor to C++
Why? | Goals | Status | Getting started | Join us
See our announcement video from CppNorth. Note that Carbon is not ready for use.
Fast and works with C++
- Performance matching C++ using LLVM, with low-level access to bits and addresses
- Interoperate with your existing C++ code, from inheritance to templates
- Fast and scalable builds that work with your existing C++ build systems
Modern and evolving
- Solid language foundations that are easy to learn, especially if you have used C++
- Easy, tool-based upgrades between Carbon versions
- Safer fundamentals, and an incremental path towards a memory-safe subset
Welcoming open-source community
- Clear goals and priorities with robust governance
- Community that works to be welcoming, inclusive, and friendly
- Batteries-included approach: compiler, libraries, docs, tools, package manager, and more
Why build Carbon?
C++ remains the dominant programming language for performance-critical software, with massive and growing codebases and investments. However, it is struggling to improve and meet developers' needs, as outlined above, in no small part due to accumulating decades of technical debt. Incrementally improving C++ is extremely difficult, both due to the technical debt itself and challenges with its evolution process. The best way to address these problems is to avoid inheriting the legacy of C or C++ directly, and instead start with solid language foundations like modern generics system, modular code organization, and consistent, simple syntax.
Existing modern languages already provide an excellent developer experience: Go, Swift, Kotlin, Rust, and many more. Developers that can use one of these existing languages should. Unfortunately, the designs of these languages present significant barriers to adoption and migration from C++. These barriers range from changes in the idiomatic design of software to performance overhead.
Carbon is fundamentally a successor language approach, rather than an attempt to incrementally evolve C++. It is designed around interoperability with C++ as well as large-scale adoption and migration for existing C++ codebases and developers. A successor language for C++ requires:
- Performance matching C++, an essential property for our developers.
- Seamless, bidirectional interoperability with C++, such that a library anywhere in an existing C++ stack can adopt Carbon without porting the rest.
- A gentle learning curve with reasonable familiarity for C++ developers.
- Comparable expressivity and support for existing software's design and architecture.
- Scalable migration, with some level of source-to-source translation for idiomatic C++ code.
With this approach, we can build on top of C++'s existing ecosystem, and bring along existing investments, codebases, and developer populations. There are a few languages that have followed this model for other ecosystems, and Carbon aims to fill an analogous role for C++:
- JavaScript → TypeScript
- Java → Kotlin
- C++ → Carbon
Language Goals
We are designing Carbon to support:
- Performance-critical software
- Software and language evolution
- Code that is easy to read, understand, and write
- Practical safety and testing mechanisms
- Fast and scalable development
- Modern OS platforms, hardware architectures, and environments
- Interoperability with and migration from existing C++ code
While many languages share subsets of these goals, what distinguishes Carbon is their combination.
We also have explicit non-goals for Carbon, notably including:
- A stable application binary interface (ABI) for the entire language and library
- Perfect backwards or forwards compatibility
Our detailed goals document fleshes out these ideas and provides a deeper view into our goals for the Carbon project and language.
Project status
Carbon Language is currently an experimental project. There is no working compiler or toolchain. You can see the demo interpreter for Carbon on compiler-explorer.com.
We want to better understand whether we can build a language that meets our successor language criteria, and whether the resulting language can gather a critical mass of interest within the larger C++ industry and community.
Currently, we have fleshed out several core aspects of both Carbon the project and the language:
- The strategy of the Carbon Language and project.
- An open-source project structure, governance model, and evolution process.
- Critical and foundational aspects of the language design informed by our
experience with C++ and the most difficult challenges we anticipate. This
includes designs for:
- Generics
- Class types
- Inheritance
- Operator overloading
- Lexical and syntactic structure
- Code organization and modular structure
- A prototype interpreter demo that can both run isolated examples and gives a detailed analysis of the specific semantic model and abstract machine of Carbon. We call this the Carbon Explorer.
If you're interested in contributing, we would love help completing the 0.1 language designs, and completing the Carbon Explorer implementation of this design. We are also currently working to get more broad feedback and participation from the C++ community. Beyond that, we plan to prioritize C++ interoperability and a realistic toolchain that implements the 0.1 language and can be used to evaluate Carbon in more detail.
You can see our full roadmap for more details.
Carbon and C++
If you're already a C++ developer, Carbon should have a gentle learning curve. It is built out of a consistent set of language constructs that should feel familiar and be easy to read and understand.
C++ code like this:
corresponds to this Carbon code:
You can call Carbon from C++ without overhead and the other way around. This means you migrate a single C++ library to Carbon within an application, or write new Carbon on top of your existing C++ investment. For example:
Read more about C++ interop in Carbon.
Beyond interoperability between Carbon and C++, we're also planning to support migration tools that will mechanically translate idiomatic C++ code into Carbon code to help you switch an existing C++ codebase to Carbon.
Generics
Carbon provides a modern generics system with checked definitions, while still supporting opt-in templates for seamless C++ interop. Checked generics provide several advantages compared to C++ templates:
- Generic definitions are fully type-checked, removing the need to
instantiate to check for errors and giving greater confidence in code.
- Avoids the compile-time cost of re-checking the definition for every instantiation.
- When using a definition-checked generic, usage error messages are clearer, directly showing which requirements are not met.
- Enables automatic, opt-in type erasure and dynamic dispatch without a separate implementation. This can reduce the binary size and enables constructs like heterogeneous containers.
- Strong, checked interfaces mean fewer accidental dependencies on implementation details and a clearer contract for consumers.
Without sacrificing these advantages, Carbon generics support specialization, ensuring it can fully address performance-critical use cases of C++ templates. For more details about Carbon's generics, see their design.
In addition to easy and powerful interop with C++, Carbon templates can be constrained and incrementally migrated to checked generics at a fine granularity and with a smooth evolutionary path.
Memory safety
Safety, and especially memory safety, remains a key challenge for C++ and something a successor language needs to address. Our initial priority and focus is on immediately addressing important, low-hanging fruit in the safety space:
- Tracking uninitialized states better, increased enforcement of initialization, and systematically providing hardening against initialization bugs when desired.
- Designing fundamental APIs and idioms to support dynamic bounds checks in debug and hardened builds.
- Having a default debug build mode that is both cheaper and more comprehensive than existing C++ build modes even when combined with Address Sanitizer.
Once we can migrate code into Carbon, we will have a simplified language with room in the design space to add any necessary annotations or features, and infrastructure like generics to support safer design patterns. Longer term, we will build on this to introduce a safe Carbon subset. This will be a large and complex undertaking, and won't be in the 0.1 design. Meanwhile, we are closely watching and learning from efforts to add memory safe semantics onto C++ such as Rust-inspired lifetime annotations.
Getting started
As there is no compiler yet, to try out Carbon, you can use the Carbon explorer to interpret Carbon code and print its output. You can try it out immediately at compiler-explorer.com.
To build the Carbon explorer yourself, you'll need to install dependencies (Bazel, Clang, libc++), and then you can run:
# Download Carbon's code.
$ git clone https://github.com/carbon-language/carbon-lang
$ cd carbon-lang
# Build and run the explorer.
$ bazel run //explorer -- ./explorer/testdata/print/format_only.carbon
For complete instructions, including installing dependencies, see our contribution tools documentation.
Learn more about the Carbon project:
Conference talks
Past Carbon focused talks from the community:
2022
- Carbon Language: An experimental successor to C++, CppNorth
- Carbon Language: Syntax and trade-offs, Core C++
2023
- Carbon’s Successor Strategy: From C++ interop to memory safety, C++Now
- Definition-Checked Generics (Part 1, Part 2), C++Now
- Modernizing Compiler Design for Carbon’s Toolchain, C++Now
Join us
We'd love to have folks join us and contribute to the project. Carbon is committed to a welcoming and inclusive environment where everyone can contribute.
- Most of Carbon's design discussions occur on Discord.
- Carbon is a Google Summer of Code 2023 organization.
- To watch for major release announcements, subscribe to our Carbon release post on GitHub and star carbon-lang.
- See our code of conduct and contributing guidelines for information about the Carbon development community.
Contributing
You can also directly:
- Contribute to the language design: feedback on design, new design proposal
- Contribute to the language implementation
- Carbon Explorer: bug report, bug fix, language feature implementation
- Carbon Toolchain, and project infrastructure
You can check out some
"good first issues",
or join the #contributing-help channel on
Discord. See our full
CONTRIBUTING documentation for more details.
