Published equation contexts

$U_{\mathrm{compiler}} \in (0,1]$

Why this formula appears here

Here $T_{\mathrm{peak}}$ is the arithmetic identity fixed at design time, $U_{\mathrm{hw}}$ $\in$ (0,1] is the fraction of cycles the hardware keeps its arithmetic units genuinely busy on whatever workload is thrown at it, and $U_{\mathrm{compiler}}$ $\in$ (0,1] is the fraction of a program’s theoretically available parallelism that the compiler or mapper actually manages to expose to the hardware. A GPU’s SIMT scheduler mostly targets $U_{\mathrm{hw}}$ , hiding latency and filling gaps dynamically regardless of how well the source program was written. A systolic array and a statically scheduled dataflow chip push almost the entire burden onto $U_{\mathrm{compiler}}$ : there is no runtime mechanism left…

Read the full article-specific guide →

Read the representative guide

U_{\mathrm{compiler}}

Symbol U_compiler

$U_c$ ompiler is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Read this term in its guide →

How to interpret it

Read this expression with the definitions, units, and assumptions supplied by the article.

Research cited beside this formula

[10] Efficient Processing of Deep Neural Networks: A Tutorial and Survey ↗

Published contexts (1)

A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.

U_{\mathrm{compiler}} \in (0,1]

Equation 4 · Semiconductors

Comparing the Main Approaches to AI Accelerator Architecture

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

Here $T_{\mathrm{peak}}$ is the arithmetic identity fixed at design time, $U_{\mathrm{hw}}$ $\in$ (0,1] is the fraction of cycles the hardware keeps its arithmetic units genuinely busy on whatever workload is thrown at it, and $U_{\mathrm{compiler}}$ $\in$ (0,1] is the fraction of a program’s theoretically available parallelism that the compiler or mapper actually manages to expose to the hardware. A GPU’s SIMT scheduler mostly targets $U_{\mathrm{hw}}$ , hiding latency and filling gaps dynamically regardless of how well the source program was written. A systolic array and a statically scheduled dataflow chip push almost the entire burden onto $U_{\mathrm{compiler}}$ : there is no runtime mechanism left…

Equation guide → · Article →

Ucompiler∈(0,1]U_{\mathrm{compiler}} \in (0,1]Ucompiler​∈(0,1]