Made for Streaming Workloads.

ArrayFire keeps frames, samples, and tensors moving through GPU and CPU pipelines with sustained throughput and minimal staging.

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Continuous compute

Build for the hundred-thousandth frame, not only the first.

ArrayFire provides a C++-native compute layer for applications that continuously process video, sensor feeds, radar returns, SDR data, and rolling analytics. Keep data resident, compose work into larger operations, and design around sustained throughput instead of repeated setup and transfer overhead.

Residentkeep working sets near the compute device
Fusedcombine compatible expressions before execution
Nativeintegrate directly into long-running C++ systems

A pipeline designed around data movement

Streaming performance depends on more than one fast kernel. Acquisition, transfers, batching, compute, and backpressure must work as one system.

AcquireReceive frames, samples, tensors, or rolling windows from the live source.
Stage onceMove data to the selected device and avoid unnecessary host round trips.
Compose computeApply transforms, filters, reductions, and model-adjacent operations.
Emit resultsReturn compact outputs or feed the next native stage with controlled latency.

Workloads that benefit

The common thread is a long-running application with repeated operations and a meaningful cost for every avoidable copy, allocation, synchronization point, or language boundary.

VISION

Video and imaging

Process frame-by-frame filters, transforms, feature extraction, and measurement without rebuilding the pipeline around each operation.

RF

Signal and radar

Compose FFTs, filtering, beamforming, thresholding, and detection inside sustained sample pipelines.

EDGE

Sensor and edge systems

Use a native compute layer where predictable resource use and deployment simplicity matter as much as peak throughput.

The controls remain visible

ArrayFire shortens the code used to express computation without pretending that streaming architecture is automatic.

Engineering leverArrayFire contributionYour system decision
Memory residencyDevice arrays and managed operation flowBuffer ownership, lifetime, and transfer boundaries
Work compositionArray expressions and JIT fusionBatch size, synchronization, and stage boundaries
Hardware targetCUDA, OpenCL, oneAPI, and CPU backendsDeployment backend and target-specific validation
Native integrationC and C++ APIs plus device interoperabilityThreading, queues, backpressure, and application lifecycle

Start with the library. Bring us the hard pipeline.

Install ArrayFire for your target system, or talk with the team about throughput, latency, and integration constraints.

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