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Multi-GPU Load Balancing

14 Nis 2017
1 dakikada okunur

Let your OpenCL kernel code run on all devices concurrently and efficiently with work partitioning for each new compute method call.

Whenever a compute method with same compute-id parameter is called, it retains older calls' work partitioning percentages and makes it better for the next call. This way, a work is iteratively load balanced on all OpenCL-enabled devices and work partitioning ratio convergences to a latency-minimizing point. This is how a developer can speed-up a repeated algorithm by 1000x.

 
 
 

Yorumlar

Why GPGPU? 

  • Offload image-resize to all GPUs and FPGAs so server feels more relaxed to host websites

  • Move compute-heavy sql table joins to C# side to let sql server handle the data-heavy parts.

  • Make particle physics programs performance-aware, even a mild overclock to one of GPUs will increase overall performance.

  • Write your own genuine kernel code to accomplish multi-GPU computing, easily without getting low-level on host side.

 UPCOMING VERSIONS: 

 

  • Device to device pipelining

 

  • Built-in image resizer functions.

 

  • Built-in matrix-multiplication functions.

 

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