Tessera performs kernel-granularity disaggregation on heterogeneous GPUs, achieving up to 2.3x throughput and 1.6x cost efficiency gains for large model inference while generalizing beyond prior methods.
In IEEE International Symposium on Workload Characterization (IISWC 2009)
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PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
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Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
Tessera performs kernel-granularity disaggregation on heterogeneous GPUs, achieving up to 2.3x throughput and 1.6x cost efficiency gains for large model inference while generalizing beyond prior methods.