| Taskflow: A General-purpose Task-parallel Programming System

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Speculative Execution

We implement a speculative execution pattern using condition tasks to route a batch of queries through a fast approximate path or a slow exact path based on a runtime probe. This example demonstrates how condition tasks express performance-driven branching — not just correctness-driven control flow — and how to correctly wire the convergence of two exclusive branches into a single downstream task without deadlock.

# Motivation

Many real-world computations have two valid execution strategies for the same result: a fast path that succeeds most of the time with low cost, and a slow fallback that is always correct but expensive. Classic examples include:

- A Bloom filter probed before a full hash-table lookup: the filter answers definitely not present or probably present in O(1) without touching the backing store. A negative answer routes directly to a cheap "not found" handler. A positive answer (which may be a false positive) routes to the full lookup to confirm.
- A branch predictor in CPU microarchitecture: the processor speculatively executes the predicted path while the branch condition is still being evaluated, rolling back only on mis-prediction.
- A cache probe before a database query: if the key is in an in-process LRU cache the result is returned immediately; on a miss the full query is issued.

In all three cases the routing decision is made by a cheap probe whose cost is negligible compared to the fallback. The structure is always: probe → fast path or slow path → merge result. This is exactly the branching structure condition tasks are designed to express.

# The Problem: Bloom Filter Lookup

We process a batch of `N` string keys against a set stored in a hash table. Before querying the hash table, each key is probed against a Bloom filter — a space-efficient probabilistic data structure that can definitively rule out absent keys in O(k) hash operations, where `k` is the number of hash functions.

The two execution paths per key are:

- Fast path (Bloom filter says not present): the key is definitely absent. Return not found immediately without touching the hash table.
- Slow path (Bloom filter says probably present): the key may be in the table. Perform the full hash-table lookup to confirm or deny.

The Bloom filter has no false negatives: if it says not present, the key is guaranteed absent. It may have false positives: a key the filter calls probably present may still be absent, in which case the hash-table lookup returns not found after doing the full work. The fast-path short circuit is therefore exact in the negative case and conservative (but correct) in the positive case.

# Per-Item Graph Structure

For a single key the task graph is a diamond: a condition task (`speculate`) probes the Bloom filter and routes to either `fast_handler` or `slow_fallback`, both of which must then converge on a single `merge` point before the result is recorded.

A naive first attempt makes `fast_handler` and `slow_fallback` regular tasks and feeds both into `merge` via strong edges:

This graph deadlocks. Task `merge` has two strong dependencies — one from `fast_handler` and one from `slow_fallback` — but only the branch selected by `speculate` will actually execute. The other branch is never scheduled, so `merge's` strong dependency counter drops from 2 to 1 and never reaches 0. `merge` waits forever. This is Pitfall 2 from Avoid Common Pitfalls applied to the switch pattern: just as both the switch task and each case task must be condition tasks in a switch control flow (see Implement Switch Control Flow), here both the routing task and the two branch tasks must be condition tasks so that their outgoing edges to `merge` are weak, bypassing the strong dependency counter entirely.

The correct per-item graph is:

Every node that has an outgoing edge to `merge` is a condition task. `merge` itself is a condition task that always returns `0` to route to `done`, making the `merge→done` edge weak. The dependency counts for the single-item graph are:

Task | Strong | Weak | Total
--- | --- | --- | ---
prepare | 0 | 0 | 0
speculate | 1 | 0 | 1
fast_handler | 0 | 1 | 1
slow_fallback | 0 | 1 | 1
merge | 0 | 2 | 2
done | 0 | 1 | 1

`merge` has two weak incoming edges but only one fires per execution, so it is enqueued exactly once. `done` has one weak incoming edge from `merge` and is also enqueued exactly once.

# Batch Graph: N Keys in Parallel

For a batch of `N` keys, each key gets its own independent (`speculate`, `fast_handler`, `slow_fallback`, `merge`, `done`) chain. The `N` chains have no mutual dependencies and execute fully in parallel across available workers. A final `collect` task gathers results; it waits for all `N` `done` tasks via strong dependencies, so it is enqueued exactly once after every chain has completed.

Because `done` is reached via a chain of weak edges (`merge→done` is weak), `done` itself has zero strong dependencies. It is a regular task, so its outgoing edge to `collect` is strong. This gives `collect` exactly `N` strong dependencies, one per chain, and it runs once all keys have been processed.

# Implementation

We implement a minimal Bloom filter and a hash-table backing store. For each key in the batch we build the five-task chain and wire it into the taskflow. Results are recorded into a per-key array so that `collect` can aggregate without any synchronization.

#include <taskflow/taskflow.hpp>
#include <bitset>
#include <unordered_set>
#include <functional>
#include <string>
#include <vector>
// ── Bloom filter ────────────────────────────────────────────────────
// A minimal Bloom filter using two independent hash functions and a
// fixed-size bit array. Real implementations use k > 2 hash functions
// and tune the array size to the expected element count and target FPR.
struct BloomFilter {
static constexpr size_t M = 1024; // bit-array size
std::bitset<M> bits;
static size_t h1(const std::string& s) {
return std::hash<std::string>{}(s) % M;
}
static size_t h2(const std::string& s) {
return (std::hash<std::string>{}(s) * 2654435761ULL) % M;
}
void insert(const std::string& s) {
bits.set(h1(s));
bits.set(h2(s));
}
// Returns false if s is definitely absent; true if probably present.
bool probe(const std::string& s) const {
return bits.test(h1(s)) && bits.test(h2(s));
}
};
// ── Result tag ──────────────────────────────────────────────────────
enum class Result { NOT_FOUND, FOUND };
int main() {
// ── build the backing store and Bloom filter ─────────────────────
std::unordered_set<std::string> table = {
"alpha", "bravo", "charlie", "delta", "echo"
};
BloomFilter bloom;
for(auto& s : table) bloom.insert(s);
// ── query batch ──────────────────────────────────────────────────
// Mix of present keys, absent keys, and keys that may be false
// positives in the Bloom filter.
std::vector<std::string> keys = {
"alpha", // present
"foxtrot", // absent, Bloom filter correctly says "not present"
"bravo", // present
"golf", // absent, may be a Bloom filter false positive
"charlie", // present
"hotel", // absent
};
const int N = static_cast<int>(keys.size());
std::vector<Result> results(N, Result::NOT_FOUND);
tf::Executor executor;
tf::Taskflow taskflow("speculative-lookup");
// ── build one chain per key ──────────────────────────────────────
std::vector<tf::Task> done_tasks(N);
for(int i = 0; i < N; i++) {
// prepare: a named anchor task, zero dependencies.
tf::Task prepare = taskflow.emplace([](){})
.name("prepare_" + std::to_string(i));
// speculate: probe the Bloom filter.
// returns 0 -> fast_handler (definitely absent)
// returns 1 -> slow_fallback (probably present, do full lookup)
tf::Task speculate = taskflow.emplace([&, i]() -> int {
return bloom.probe(keys[i]) ? 1 : 0;
}).name("speculate_" + std::to_string(i));
// fast_handler: Bloom filter says "not present" — key is absent.
// Condition task returning 0 -> merge (weak edge, avoids deadlock).
tf::Task fast_handler = taskflow.emplace([&, i]() -> int {
results[i] = Result::NOT_FOUND;
printf("[fast] key='%s' -> not found (Bloom filter negative)\n",
keys[i].c_str());
return 0;
}).name("fast_" + std::to_string(i));
// slow_fallback: Bloom filter says "probably present" — do full lookup.
// Condition task returning 0 -> merge (weak edge, avoids deadlock).
tf::Task slow_fallback = taskflow.emplace([&, i]() -> int {
bool found = table.count(keys[i]) > 0;
results[i] = found ? Result::FOUND : Result::NOT_FOUND;
printf("[slow] key='%s' -> %s (full lookup)\n",
keys[i].c_str(), found ? "found" : "not found (false positive)");
return 0;
}).name("slow_" + std::to_string(i));
// merge: convergence point for fast and slow paths.
// Condition task returning 0 -> done (weak edge).
// Has two weak incoming edges; exactly one fires per execution.
tf::Task merge = taskflow.emplace([]() -> int {
return 0;
}).name("merge_" + std::to_string(i));
// done: regular task, records completion.
// Has one weak incoming edge from merge; strong outgoing edge to collect.
tf::Task done = taskflow.emplace([&, i](){
printf("[done] key='%s'\n", keys[i].c_str());
}).name("done_" + std::to_string(i));
done_tasks[i] = done;
// ── wire the chain ─────────────────────────────────────────────
speculate.succeed(prepare); // strong: prepare -> speculate
speculate.precede(fast_handler, slow_fallback); // weak: 0=fast, 1=slow
fast_handler.precede(merge); // weak: fast -> merge (return 0)
slow_fallback.precede(merge); // weak: slow -> merge (return 0)
merge.precede(done); // weak: merge -> done (return 0)
}
// ── collect: waits for all N done tasks via strong dependencies ──
tf::Task collect = taskflow.emplace([&](){
int found = 0;
for(auto& r : results) if(r == Result::FOUND) found++;
printf("\nSummary: %d/%d keys found\n", found, N);
}).name("collect");
// done_tasks[i] -> collect: strong edges, collect enqueued exactly once
for(auto& d : done_tasks) d.precede(collect);
executor.run(taskflow).wait();
return 0;
}
tf::Executor::run
tf::Future< void > run(Taskflow &taskflow)
runs a taskflow once
tf::Task::succeed
Task & succeed(Ts &&... tasks)
adds precedence links from other tasks to this
Definition task.hpp:1313
tf::Task::precede
Task & precede(Ts &&... tasks)
adds precedence links from this to other tasks
Definition task.hpp:1305

A sample run with the keys above produces output such as:

[fast] key='foxtrot' -> not found (Bloom filter negative)
[slow] key='alpha' -> found (full lookup)
[fast] key='hotel' -> not found (Bloom filter negative)
[slow] key='bravo' -> found (full lookup)
[slow] key='golf' -> not found (false positive)
[slow] key='charlie' -> found (full lookup)
[done] key='foxtrot'
[done] key='alpha'
[done] key='hotel'
[done] key='bravo'
[done] key='golf'
[done] key='charlie'
Summary: 3/6 keys found

The fast and slow paths for different keys interleave freely because the `N` chains are fully independent. The `collect` task appears last, after all `done` tasks have completed.

# Design Points

- Both the routing task and each branch task must be condition tasks: The deadlock in `spec_wrong` arises because `fast_handler` and `slow_fallback` are regular tasks, making their edges to `merge` strong. `merge` then waits for two strong dependencies that can never both be satisfied. The fix is for every task that has an outgoing edge to a shared convergence point to be a condition task, so those edges are weak and bypass the strong dependency counter. This is the same rule as for switch control flow (see Implement Switch Control Flow): whenever a condition task fans out to `k` branches that all converge on the same downstream task, all `k` branch tasks must also be condition tasks.
- `merge` is a pass-through condition task: `merge` performs no computation — it exists solely as a controlled convergence point. It has two incoming weak edges (exactly one fires) and one outgoing weak edge to `done`. Its return value is always `0`, which is the index of its only successor. This pattern — a no-op condition task used purely to merge exclusive branches — is the condition-task analogue of the auxiliary join task used for the same purpose with regular tasks in Incremental Build Graph.
- `done` bridges the condition-task chain to the strong-dependency world: `collect` needs strong incoming edges so that it is enqueued exactly once after all `N` chains have completed. `done` is a regular task reached via a weak edge from `merge`, so its outgoing edge to `collect` is strong. It serves as the re-entry point from the condition-task world (all weak edges, activated by routing) back to the regular-task world (strong edges, activated by reference counting). Without `done`, `collect` would need `N` weak incoming edges from `merge` and would be enqueued up to `N` times simultaneously — a task race analogous to Pitfall 2 from Avoid Common Pitfalls.
- The fast-path short circuit is a scheduling gain, not just a code organisation gain: When `speculate` returns `0`, the scheduler enqueues `fast_handler` immediately and never touches `slow_fallback`. `slow_fallback` is never dequeued, never invoked, and its slot in the worker queue is never occupied. On a workload where 90% of keys are absent and the Bloom filter has a low false-positive rate, 90% of the per-key chains skip the hash-table lookup entirely, and those workers are freed to pick up the next available chain sooner. This throughput gain is automatic: it requires no explicit thread management, no condition variables, and no external job queue.

Note
  The Bloom filter in this example uses two hash functions for clarity. Production Bloom filters typically use `k` = ln(2) * m/n hash functions, where `m` is the bit-array size and `n` is the number of inserted elements, to minimise the false-positive rate for a given memory budget. The graph structure and condition task wiring are identical regardless of `k`; only the `probe` function changes.