* Optimize batching of transactions during replay for parallel processing (cherry picked from commit4de14e530b
) * fix build (cherry picked from commitdfef68f985
) * updates to address review feedback (cherry picked from commitc5d8560cdb
) * suppress clippy (cherry picked from commita146f2d853
) Co-authored-by: Pankaj Garg <pankaj@solana.com>
This commit is contained in:
@ -28,6 +28,7 @@ use {
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bank_utils,
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block_cost_limits::*,
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commitment::VOTE_THRESHOLD_SIZE,
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cost_model::CostModel,
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snapshot_config::SnapshotConfig,
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snapshot_package::{AccountsPackageSender, SnapshotType},
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snapshot_utils::{self, BankFromArchiveTimings},
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@ -53,6 +54,7 @@ use {
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collect_token_balances, TransactionTokenBalancesSet,
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},
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std::{
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borrow::Cow,
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cell::RefCell,
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collections::{HashMap, HashSet},
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path::PathBuf,
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@ -247,7 +249,7 @@ fn execute_batch(
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first_err.map(|(result, _)| result).unwrap_or(Ok(()))
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}
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fn execute_batches(
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fn execute_batches_internal(
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bank: &Arc<Bank>,
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batches: &[TransactionBatch],
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entry_callback: Option<&ProcessCallback>,
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@ -290,6 +292,75 @@ fn execute_batches(
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first_err(&results)
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}
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fn execute_batches(
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bank: &Arc<Bank>,
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batches: &[TransactionBatch],
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entry_callback: Option<&ProcessCallback>,
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transaction_status_sender: Option<&TransactionStatusSender>,
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replay_vote_sender: Option<&ReplayVoteSender>,
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timings: &mut ExecuteTimings,
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cost_capacity_meter: Arc<RwLock<BlockCostCapacityMeter>>,
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) -> Result<()> {
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let lock_results = batches
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.iter()
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.flat_map(|batch| batch.lock_results().clone())
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.collect::<Vec<_>>();
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let sanitized_txs = batches
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.iter()
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.flat_map(|batch| batch.sanitized_transactions().to_vec())
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.collect::<Vec<_>>();
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let cost_model = CostModel::new();
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let mut minimal_tx_cost = u64::MAX;
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let mut total_cost: u64 = 0;
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// Allowing collect here, since it also computes the minimal tx cost, and aggregate cost.
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// These two values are later used for checking if the tx_costs vector needs to be iterated over.
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#[allow(clippy::needless_collect)]
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let tx_costs = sanitized_txs
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.iter()
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.map(|tx| {
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let cost = cost_model.calculate_cost(tx).sum();
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minimal_tx_cost = std::cmp::min(minimal_tx_cost, cost);
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total_cost = total_cost.saturating_add(cost);
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cost
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})
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.collect::<Vec<_>>();
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let target_batch_count = get_thread_count() as u64;
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let mut tx_batches: Vec<TransactionBatch> = vec![];
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let rebatched_txs = if total_cost > target_batch_count.saturating_mul(minimal_tx_cost) {
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let target_batch_cost = total_cost / target_batch_count;
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let mut batch_cost: u64 = 0;
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let mut slice_start = 0;
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tx_costs.into_iter().enumerate().for_each(|(index, cost)| {
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let next_index = index + 1;
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batch_cost = batch_cost.saturating_add(cost);
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if batch_cost >= target_batch_cost || next_index == sanitized_txs.len() {
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let txs = &sanitized_txs[slice_start..=index];
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let results = &lock_results[slice_start..=index];
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let tx_batch = TransactionBatch::new(results.to_vec(), bank, Cow::from(txs));
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slice_start = next_index;
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tx_batches.push(tx_batch);
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batch_cost = 0;
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}
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});
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&tx_batches[..]
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} else {
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batches
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};
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execute_batches_internal(
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bank,
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rebatched_txs,
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entry_callback,
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transaction_status_sender,
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replay_vote_sender,
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timings,
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cost_capacity_meter,
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)
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}
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/// Process an ordered list of entries in parallel
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/// 1. In order lock accounts for each entry while the lock succeeds, up to a Tick entry
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/// 2. Process the locked group in parallel
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