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feat: Avoiding redundant computation in PG (#5844)
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This PR reduces PG computation time by removing computation on indices
over which accumulated relation values are expected to be zero. This
gives us a speedup of 4-5%. The PR also parallelised pertubator root
construction.
Before:
x86_64:

![image](https://github.com/AztecProtocol/aztec-packages/assets/4798775/80247864-1c1c-4e34-8756-a8bd44bdbab2)
wasm:

![image](https://github.com/AztecProtocol/aztec-packages/assets/4798775/649dfd97-d65c-48a5-8b8c-02fb3fbb9f47)
After:
x86_64:

![image](https://github.com/AztecProtocol/aztec-packages/assets/4798775/d453e026-7a88-4646-8094-b7102916a2af)
wasm:

![image](https://github.com/AztecProtocol/aztec-packages/assets/4798775/549b89a3-d1dd-4058-84a2-92856180d15d)
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@Rumata888
Rumata888 authored Apr 30, 2024
1 parent 1904fa8 commit 9f57733
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Showing 11 changed files with 445 additions and 144 deletions.
15 changes: 10 additions & 5 deletions barretenberg/cpp/src/barretenberg/flavor/flavor.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -251,18 +251,23 @@ template <typename Tuple, std::size_t Index = 0> static constexpr size_t compute
* @details The size of the outer tuple is equal to the number of relations. Each relation contributes an inner tuple of
* univariates whose size is equal to the number of subrelations of the relation. The length of a univariate in an inner
* tuple is determined by the corresponding subrelation length and the number of instances to be folded.
* @tparam optimised Enable optimised version with skipping some of the computation
*/
template <typename Tuple, size_t NUM_INSTANCES, size_t Index = 0>
template <typename Tuple, size_t NUM_INSTANCES, bool optimised = false, size_t Index = 0>
static constexpr auto create_protogalaxy_tuple_of_tuples_of_univariates()
{
if constexpr (Index >= std::tuple_size<Tuple>::value) {
return std::tuple<>{}; // Return empty when reach end of the tuple
} else {
using UnivariateTuple =
typename std::tuple_element_t<Index,
Tuple>::template ProtogalaxyTupleOfUnivariatesOverSubrelations<NUM_INSTANCES>;
return std::tuple_cat(std::tuple<UnivariateTuple>{},
create_protogalaxy_tuple_of_tuples_of_univariates<Tuple, NUM_INSTANCES, Index + 1>());
std::conditional_t<optimised,
typename std::tuple_element_t<Index, Tuple>::
template OptimisedProtogalaxyTupleOfUnivariatesOverSubrelations<NUM_INSTANCES>,
typename std::tuple_element_t<Index, Tuple>::
template ProtogalaxyTupleOfUnivariatesOverSubrelations<NUM_INSTANCES>>;
return std::tuple_cat(
std::tuple<UnivariateTuple>{},
create_protogalaxy_tuple_of_tuples_of_univariates<Tuple, NUM_INSTANCES, optimised, Index + 1>());
}
}

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214 changes: 137 additions & 77 deletions barretenberg/cpp/src/barretenberg/polynomials/univariate.hpp
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44 changes: 27 additions & 17 deletions barretenberg/cpp/src/barretenberg/protogalaxy/combiner.test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,12 @@ TEST(Protogalaxy, CombinerOn2Instances)
auto prover_polynomials = get_sequential_prover_polynomials<Flavor>(
/*log_circuit_size=*/1, idx * 128);
restrict_to_standard_arithmetic_relation(prover_polynomials);
// This ensures that the combiner accumulator for second instance = 0
// The value is computed by generating the python script values, computing the resulting accumulator and
// taking the value at index 1
if (idx == NUM_INSTANCES - 1) {
prover_polynomials.q_c[0] -= 13644570;
}
instance->proving_key.polynomials = std::move(prover_polynomials);
instance->proving_key.circuit_size = 2;
instance_data[idx] = instance;
Expand All @@ -52,22 +58,22 @@ TEST(Protogalaxy, CombinerOn2Instances)
ProverInstances instances{ instance_data };
instances.alphas.fill(bb::Univariate<FF, 12>(FF(0))); // focus on the arithmetic relation only
auto pow_polynomial = PowPolynomial(std::vector<FF>{ 2 });
auto result = prover.compute_combiner(instances, pow_polynomial);
auto expected_result = Univariate<FF, 12>(std::array<FF, 12>{
87706,
13644570,
76451738,
226257946,
static_cast<uint64_t>(500811930),
static_cast<uint64_t>(937862426),
static_cast<uint64_t>(1575158170),
static_cast<uint64_t>(2450447898),
static_cast<uint64_t>(3601480346),
static_cast<uint64_t>(5066004250),
static_cast<uint64_t>(6881768346),
static_cast<uint64_t>(9086521370),
});
auto result = prover.compute_combiner</*OptimisationEnabled=*/false>(instances, pow_polynomial);
auto optimised_result = prover.compute_combiner(instances, pow_polynomial);
auto expected_result = Univariate<FF, 12>(std::array<FF, 12>{ 87706,
0,
0x02ee2966,
0x0b0bd2cc,
0x00001a98fc32,
0x000033d5a598,
0x00005901cefe,
0x00008c5d7864,
0x0000d028a1ca,
0x000126a34b30UL,
0x0001920d7496UL,
0x000214a71dfcUL });
EXPECT_EQ(result, expected_result);
EXPECT_EQ(optimised_result, expected_result);
} else {
std::vector<std::shared_ptr<ProverInstance>> instance_data(NUM_INSTANCES);
ProtoGalaxyProver prover;
Expand Down Expand Up @@ -130,11 +136,13 @@ TEST(Protogalaxy, CombinerOn2Instances)
0 0 0 0 0 0 0 0 0 6 18 36 60 90 */

auto pow_polynomial = PowPolynomial(std::vector<FF>{ 2 });
auto result = prover.compute_combiner(instances, pow_polynomial);
auto result = prover.compute_combiner</*OptimisationEnabled=*/false>(instances, pow_polynomial);
auto optimised_result = prover.compute_combiner(instances, pow_polynomial);
auto expected_result =
Univariate<FF, 12>(std::array<FF, 12>{ 0, 0, 12, 36, 72, 120, 180, 252, 336, 432, 540, 660 });

EXPECT_EQ(result, expected_result);
EXPECT_EQ(optimised_result, expected_result);
}
};
run_test(true);
Expand Down Expand Up @@ -181,11 +189,13 @@ TEST(Protogalaxy, CombinerOn4Instances)
zero_all_selectors(instances[3]->proving_key.polynomials);

auto pow_polynomial = PowPolynomial(std::vector<FF>{ 2 });
auto result = prover.compute_combiner(instances, pow_polynomial);
auto result = prover.compute_combiner</*OptimisationEnabled=*/false>(instances, pow_polynomial);
auto optimised_result = prover.compute_combiner(instances, pow_polynomial);
std::array<FF, 40> zeroes;
std::fill(zeroes.begin(), zeroes.end(), 0);
auto expected_result = Univariate<FF, 40>(zeroes);
EXPECT_EQ(result, expected_result);
EXPECT_EQ(optimised_result, expected_result);
};
run_test();
};
5 changes: 5 additions & 0 deletions barretenberg/cpp/src/barretenberg/protogalaxy/protogalaxy.test.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -279,6 +279,11 @@ template <typename Flavor> class ProtoGalaxyTests : public testing::Test {

bb::Univariate<FF, 11> expected_eta{ { 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 } };
EXPECT_EQ(instances.relation_parameters.eta, expected_eta);
// Optimised relation parameters are the same, we just don't compute any values for non-used indices when
// deriving values from them
for (size_t i = 0; i < 11; i++) {
EXPECT_EQ(instances.optimised_relation_parameters.eta.evaluations[i], expected_eta.evaluations[i]);
}
}

/**
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