Apple M5 Pro, CLI benchmark: 24.0 -> 43.2 MH/s. Kernel output is
unchanged and bit-exact against the CPU reference.
Field arithmetic:
- Deferred-carry additions (Acc): the external layer and the internal
row sum accumulate unreduced with a carry counter and fold once per
output instead of two epsilon corrections per add.
- gf64_mul_add folds the row sum into the 128-bit product before a
single plain reduction.
- mul_wide / gf64_sqr assemble the 128-bit product from 32-bit-valued
partial sums with no carry compares.
Kernel structure (code size dominates on Apple's compiler; every
unrolled variant measured slower):
- One loop drives all 30 rounds so each layer is emitted once, with
round constants added by the linear layer preceding each S-box
(RC_EXT table, RC_INTERNAL padded with a trailing zero).
- One S-box site with a runtime lane count (12 external, 1 internal).
- mining_main runs pad / first squeeze / second squeeze through a
single inlined permute64 call in a phase loop.
Measured and rejected: uniform buffers and unhoisted loads, workgroup
sizes 64/128/512, 8-add MDS, one-compare reduce, fully unrolled
layers, two nonces per thread.
Investigating reported seal submission failures. Root cause analysis:
the miner's job-id staleness chain (worker check, quic-loop filter,
node-side job_id compare) cannot mislabel a candidate — internal and
node job ids are updated atomically together. The remaining benign race
is node-side: a block-template rebuild (new pre_hash, same parent) does
not bump the node's job counter, so an in-flight seal mined on the old
template passes the job-id check and fails seal verification, logging
"Failed to submit seal". Fixing that belongs in the chain repo (bump the
job counter on template rebuild).
Miner-side hardening in this commit:
- gpu_cpu_parity is now a two-phase fuzzer. Phase 1 dumps FULL 512-bit
hashes for randomized (header, nonce) pairs via the real
midstate-resume datapath and verifies each against the canonical CPU
implementation, multi-threaded (defaults to 1M hashes, ~5s; 10M runs
in ~15s). Phase 2 fuzzes whole mining jobs with five profiles: random,
carry-edge starts (saturated low limbs), 2^256-boundary crossings,
impossible difficulty with exact hash-count assertion, and CPU-known
solutions to catch false negatives. Seeded and reproducible; also
asserts the submitted work bytes match the nonce.
Verified clean: 10M bulk hashes + ~1,500 seals across 6 seeds.
- miner-service re-verifies every candidate on the CPU (one hash) before
sending JobResult; an engine-produced invalid seal is never submitted
and is logged loudly instead.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Exploring a fully native Metal port of the mining kernel showed it runs
~16% faster than the wgpu path at equal batch size. Decomposing that gap:
~9% is naga's injected runtime bounds checks and forced loop bounding,
~1.5% is mulhi()-based multiplication (inexpressible in WGSL), <1% is
the constant address space; the rest is binding/codegen residue.
The dominant term is recoverable inside wgpu on every backend:
the engine now builds its pipelines with create_shader_module_trusted +
ShaderRuntimeChecks::unchecked(). This is sound for our shaders: sources
are compiled into the binary, every buffer access is a constant-bounded
loop index into fixed-size bindings the engine allocates itself, all
loops have static bounds, and both variants remain covered by the
dual-shader component suite and the CPU parity example.
Apple M4: 11.39 -> 12.34 MH/s at the default 1M batch (+8.3%), 11.74 ->
12.73 MH/s at 8M (+8.4%). Cumulative vs main: 4.4x.
New examples/trusted_hashrate.rs measures checked vs trusted on the same
shader and CPU-verifies results.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Evaluated all ideas from #80 against the native-u64 kernel. Its three
main wins (lazy field arithmetic, precomputed sponge state, conditional
second squeeze) were already present in stronger form; two micro-opts
carried over and measured positive at large batches (+1.3% at 16M,
noise-level at the 1M default):
- Hoist midstate/target/start-nonce storage reads out of the nonce loop.
- Produce byte-swapped hash words on demand during the target compare,
so the reject path skips building the swapped hash entirely.
Forcing nonces_per_thread=2 was also tested and regressed; one thread
per nonce stays.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Three stacked optimizations, each verified bit-exact against the CPU
reference (qp-poseidon-core) by the dual-shader component suite and a
new GPU/CPU parity example:
- mining_u64.wgsl: native 64-bit Goldilocks arithmetic with plonky2-style
lazy reduction, used automatically when the adapter supports
SHADER_INT64 (all Apple Silicon, NVIDIA, modern AMD). mining.wgsl
remains the 32-bit fallback. (+50%)
- Midstate precompute: the first two of five sponge permutations absorb
the header and high nonce half, both constant per batch; they are now
computed once on the CPU (pow_core::mining_midstate) and resumed on
the GPU. Batches are clamped so nonces never carry past 2^256. (+77%)
- Lazy second squeeze: the first squeeze yields the most significant 256
bits of the hash, which decide hash-vs-target unless exactly equal to
the target's high half, so the common reject path skips the final
permutation. (+56%)
Apple M4 throughput: 2.81 -> 11.4 MH/s at the default 1M batch
(criterion large_range_1m/gpu: 355.8ms -> 86.3ms, -75.7%).
Also:
- Fix criterion GPU bench crash: thread-local worker resources are now
tagged with an engine id so multiple GpuEngines per process never mix
devices.
- Fix stale end-to-end test harness (6-binding layout) and run the full
component suite against both shader variants.
- New examples: hashrate, gpu_cpu_parity (25 jobs incl. 2^256-boundary
crossing), gpu_features.
- Add Apple M5 family GPU tiers.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* handle lost device correctly
* Update lib.rs
* allow-integrated flag otherwise exclude
* DeviceLost is a status
* Integrated GPU skipped when discrete init fails
* nits
On Windows wgpu enumerates each physical GPU once per backend (Vulkan +
DX12) plus a CPU-emulated fallback ("Microsoft Basic Render Driver").
Building a mining context for every entry causes VRAM contention and
OOMs the process during benchmark/serve startup (#61).
Instead of deduplicating by (vendor, device) PCI IDs - which would
collapse rigs with multiple identical cards into a single context - drop
CPU-emulated adapters and keep all adapters from the highest-ranked
backend present (Vulkan/Metal, then DX12). Within a single backend each
physical GPU appears exactly once, so identical cards are preserved by
construction and no physical-ID matching is needed.
Selected adapters are ordered discrete-first so `--gpu-devices 1` picks
the discrete card on hybrid laptops. Skipped adapters are logged at info
level; if nothing usable remains, init fails with an explicit error.
Selection logic is a pure index-based function unit-tested against the
exact enumeration reported in #61, identical multi-GPU rigs, DX12-only
machines, and software-only environments.
Co-authored-by: illuzen <illuzen@users.noreply.github.com>
* support AMD GPUs on windows
* add support for more GPUs
* remove shader dump from logs
* handle gpu initialization failure better
* fmt
* re-order matching to avoid mobile / desktop false positives
* harden gpu matching
* add tests to engine-gpu module
* handle dead device and failed mapping correctly
* handle timeout properly with tokio
* nits
* fix benchmarks
* fmt
* improve logging
* don't let the queue spill, just drain it
* move cancellation out of the shader, since it doesn't work
* simplify batch size mechanism, improve logs
* fmt
* address review items
* single hash, new poseidon
* fix build
* new poseidon api
* fmt
* fix race condition with stale jobs being reported
* fmt
* fix deps
* format toml
remove stale comments
---------
Co-authored-by: Nikolaus Heger <nheger@gmail.com>
* implement quic
* simpler names
* simplify worker logic
* simplify gpu configuration logic
* miner initiates and picks random start nonce
* remove mining service
* clean up logs and remove unused engines
* clean up metrics
* metrics fix
* shared worker pool, cpu driven cancellation
just 10x the gpu thru-put, no more batch-size on gpu
* fmt
* non-local git
* taplo derp
* fix the benches
* handle stale results correctly in gpu
* more accurate gpu hashrate
* fmt
* remove unused
* handle duplicate solutions, cli controls gpu cancel frequency
* put plonky2 back
* demote noisy log
Nikolaus Heger