PARALLEL COMPUTATION ENGINE
Parallengine distributes computational workloads across infinite parallel threads, leveraging a proprietary graph-based execution model. Each node in the computation graph executes independently, communicating through lock-free message channels.
The engine dynamically rebalances thread allocation based on real-time telemetry, ensuring zero-wait pipeline stages and maximum throughput across heterogeneous compute clusters.
+------------------+ +------------------+ +------------------+
| INPUT STREAM | ====> | GRAPH COMPILER | ====> | THREAD POOL |
| [data_in:0x0] | | [gc:0xAF] | | [tp:0xFF] |
+--------+---------+ +--------+---------+ +--------+---------+
| | |
v v v
+--------+---------+ +--------+---------+ +--------+---------+
| TOKENIZER | | DAG OPTIMIZER | | WORK STEALER |
| [tk:0x1A] | | [dag:0xB2] | | [ws:0xC3] |
+--------+---------+ +--------+---------+ +--------+---------+
| | |
+------------+-------------+-------------+------------+
| |
v v
+--------+---------+ +--------+---------+
| RESULT MERGER | | TELEMETRY BUS |
| [rm:0xD4] | | [tel:0xE5] |
+--------+---------+ +--------+---------+
| |
+-------------+-------------+
|
v
+---------+----------+
| OUTPUT STREAM |
| [data_out:0xF6] |
+---------------------+
Computation is modeled as a directed acyclic graph. Each node represents an isolated task with explicit data dependencies. The scheduler topologically sorts the graph and dispatches independent nodes to available threads simultaneously.
graph.execute(nodes, {parallel: true})
Inter-thread communication uses lock-free MPMC channels with zero-copy semantics. Messages are passed through atomic ring buffers, eliminating contention and ensuring sub-microsecond latency between computation stages.
channel.send(data, {mode: "zero_copy"})