mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-05-06 17:49:07 -05:00
c7bd160e15
- Changed A100-vs-H100 comparisons to H100-vs-MI300X-vs-Gaudi3 three-way - Added Hardware Zoo slide showing all 22 platforms across 6 vendors - Added timing markers [N min] to speaker notes throughout - Added "turn to your neighbor" discussion prompts after predict slides - Added roadmap "You Are Here" slides after each break - Stripped code boilerplate (import only shown once, aliases for later demos) - Added figure references for new SVGs (multi-vendor-roofline, parallelism-3d, memory-hierarchy, pareto-front) - Updated related_work.tex comparison table to highlight vendor breadth
363 lines
13 KiB
TeX
363 lines
13 KiB
TeX
% =============================================================================
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% MLSys-im ISCA 2026 Tutorial — Related Work & Positioning (~8 slides)
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% =============================================================================
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% For ISCA distribution: self-contained Metropolis theme.
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% For internal use: swap to beamerthememlsys.sty
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% =============================================================================
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% This file is designed to be \input{related_work} into tutorial_part1.tex
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% or tutorial_part2.tex. It contains no preamble or document environment.
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% =============================================================================
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% =====================================================================
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% RELATED WORK & POSITIONING
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% =====================================================================
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\section{Related Work}
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% --- Slide R.1: The Landscape ---
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\begin{frame}{The Landscape: Existing ML Performance Tools}
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\centering
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\scriptsize
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\begin{tabular}{@{}lllllll@{}}
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\toprule
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\textbf{Tool} & \textbf{Type} & \textbf{Speed} & \textbf{Scope} & \textbf{HW Coverage} & \textbf{Access} \\
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\midrule
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ASTRA-sim & Discrete-event & Hours & Network & NVIDIA & Open \\
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Calculon & Analytical & Seconds & Training & NVIDIA & Open \\
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DeepSpeed Profiler & Runtime & Real-time & Single-node & NVIDIA & Requires GPU \\
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LLMPerf / vLLM & Empirical & Minutes & Serving & NVIDIA, AMD & Requires GPU \\
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Roofline Toolkit & Empirical & Minutes & Compute/memory & Intel, NVIDIA & Requires HW \\
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Internal (Google, Meta) & Analytical & Seconds & Full-stack & Custom & Proprietary \\
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\rowcolor{crimson!12}
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\textbf{mlsysim} & \textbf{Analytical} & \textbf{Sub-second} & \textbf{22 walls} & \textbf{5 vendors} & \textbf{Open, no GPU} \\
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\bottomrule
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\end{tabular}
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\vspace{0.8em}
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\small
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\textbf{Key observations:} (1) Open tools cover 3--4 constraints each.
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(2) Most are NVIDIA-only. mlsysim covers \textbf{NVIDIA, AMD, Intel, Google, Cerebras,}
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and custom silicon---full stack from compute to carbon.
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\end{frame}
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% --- Slide R.2: What Makes mlsysim Different? ---
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\begin{frame}{What Makes mlsysim Different?}
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\begin{columns}[T]
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\column{0.55\textwidth}
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\begin{enumerate}
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\item \textbf{Breadth: 22 walls in one pipeline}\\
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\small Others cover 3--4 constraints.\\
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mlsysim composes all 22 into a single\\
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\texttt{Pipeline.solve()} call.
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\vspace{0.6em}
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\item \textbf{Unit safety: Pint dimensional analysis}\\
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\small Every quantity carries its unit.\\
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\texttt{GB + TFLOPS} $\Rightarrow$ \textcolor{crimson}{DimensionalityError}.\\
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Catches errors spreadsheets never will.
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\vspace{0.6em}
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\item \textbf{Traceability: Every constant is cited}\\
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\small \texttt{TraceableConstant} records source,\\
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date, and DOI for every hardware spec.\\
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Audit any number back to its origin.
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\end{enumerate}
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\column{0.42\textwidth}
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\centering
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\begin{block}{The Trifecta}
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\small
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\begin{tabular}{@{}lc@{}}
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Feature & Others \\
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\midrule
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22 walls & 3--4 \\
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Unit safety & None \\
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Traceability & Rare \\
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\end{tabular}
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\end{block}
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\vspace{1em}
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\begin{exampleblock}{One-Liner}
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\footnotesize
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\texttt{pip install mlsysim}\\[2pt]
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No GPU. No cluster.\\
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Just Python 3.10+.
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\end{exampleblock}
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\end{columns}
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\end{frame}
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% --- Slide R.3: The Fidelity-Speed Spectrum ---
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\begin{frame}{The Fidelity--Speed Spectrum}
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\centering
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\begin{tikzpicture}[
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every node/.style={font=\small},
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arrow/.style={-{Stealth[length=3mm]}, thick, color=midgray},
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tool/.style={draw, rounded corners=3pt, fill=computeblue, text=darktext,
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minimum height=0.7cm, minimum width=2.2cm, font=\footnotesize\bfseries},
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highlight/.style={tool, fill=crimson!15, draw=crimson, thick},
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]
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% Axis
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\draw[arrow] (0,0) -- (12.5,0) node[right] {\textbf{Speed}};
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\draw[arrow] (0,0) -- (0,4.5) node[above] {\textbf{Fidelity}};
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% Labels on axis
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\node[font=\scriptsize, color=midgray] at (1.5,-0.4) {Hours};
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\node[font=\scriptsize, color=midgray] at (5.0,-0.4) {Minutes};
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\node[font=\scriptsize, color=midgray] at (8.5,-0.4) {Seconds};
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\node[font=\scriptsize, color=midgray] at (11.5,-0.4) {Instant};
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% Tools
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\node[tool] (cycle) at (1.5, 3.8) {Cycle-accurate};
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\node[tool] (astra) at (3.2, 3.0) {ASTRA-sim};
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\node[tool] (llmperf) at (4.5, 2.2) {LLMPerf};
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\node[tool] (calculon) at (7.0, 2.5) {Calculon};
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\node[highlight] (mlsysim) at (9.0, 2.0) {mlsysim};
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\node[tool] (napkin) at (11.5, 0.8) {Napkin math};
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% Annotation for mlsysim
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\node[font=\scriptsize, color=crimson, align=center] at (9.0, 1.0)
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{22 walls, sub-second\\composable pipeline};
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% Sweet-spot region
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\draw[dashed, crimson, thick, rounded corners=6pt]
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(7.5, 0.5) rectangle (10.5, 2.8);
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\node[font=\scriptsize\itshape, color=crimson] at (9.0, 3.1)
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{``Fast analytical'' sweet spot};
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\end{tikzpicture}
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\vspace{0.3em}
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\small\itshape
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mlsysim trades cycle-accuracy for speed and breadth.\\
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The right tool depends on the question you are asking.
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\end{frame}
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% --- Slide R.4: What mlsysim Does NOT Do ---
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\begin{frame}{What mlsysim Does NOT Do}
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\begin{columns}[T]
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\column{0.52\textwidth}
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\textbf{\textcolor{crimson}{Not a replacement for:}}
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\begin{itemize}
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\item \textbf{Benchmarking} --- real measurements on real hardware
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\item \textbf{Cycle-accurate simulation} --- microarchitectural detail
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\item \textbf{Production deployment} --- no orchestration, no serving
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\item \textbf{Framework profiling} --- no kernel-level tracing
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\end{itemize}
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\vspace{0.5em}
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\textbf{Prediction accuracy:}\\
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Within \alert{2--5$\times$} of measured performance.\\
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Useful for \emph{ranking} and \emph{bounding},\\
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not for exact latency guarantees.
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\column{0.45\textwidth}
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\textbf{\textcolor{datastroke}{What it IS:}}
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\begin{itemize}
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\item A \textbf{reasoning framework}
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\item Answers ``which constraint binds?''
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\item Answers ``is this hardware sufficient?''
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\item Answers ``what should I try first?''
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\item Runs in $<$1\,ms per evaluation
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\item No GPU required
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\end{itemize}
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\vspace{0.5em}
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\begin{center}
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\small\itshape
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``All models are wrong,\\
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but some are useful.''\\
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--- George Box
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\end{center}
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\end{columns}
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\end{frame}
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% --- Slide R.5: How to Think About Accuracy ---
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\begin{frame}{How to Think About Accuracy: The CPI Analogy}
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\begin{block}{Patterson's Insight (Computer Architecture)}
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\small
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Hennessy \& Patterson did not predict CPI from first principles.\\
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They \textbf{measured} CPI empirically, then used it to \textbf{reason} about
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architectural tradeoffs. The value was in the \textbf{framework}, not the exact number.
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\end{block}
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\vspace{0.5em}
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\begin{block}{Our Approach (ML Systems)}
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\small
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We do not predict $\eta$ (efficiency) from first principles.\\
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We \textbf{measure} $\eta$ empirically (MFU from published papers), then use it
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to \textbf{reason} about system tradeoffs across 22 walls.
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\end{block}
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\vspace{0.8em}
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\centering
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\begin{tabular}{@{}lll@{}}
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\toprule
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& \textbf{Computer Architecture} & \textbf{ML Systems} \\
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\midrule
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Empirical constant & CPI & $\eta$ (MFU) \\
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Framework & Iron Law of CPU Perf & Iron Law of ML Systems \\
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Value & Reason about ISA tradeoffs & Reason about system walls \\
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\bottomrule
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\end{tabular}
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\end{frame}
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% --- Slide R.6: The Iron Law --- Our Contribution ---
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\begin{frame}{The Iron Law: Every Wall Maps to a Term}
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\begin{center}
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\large
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\[
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T = \frac{\text{FLOPs}}{N \;\times\; \text{Peak} \;\times\; \text{MFU} \;\times\; \eta_{\text{scaling}} \;\times\; \text{Goodput}}
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\]
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\end{center}
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\vspace{0.3em}
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\small
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\begin{columns}[T]
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\column{0.48\textwidth}
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\begin{tabular}{@{}ll@{}}
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\toprule
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\textbf{Term} & \textbf{Walls} \\
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\midrule
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FLOPs (numerator) & 11 Complexity \\
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& 12 Reasoning \\
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& 13 Fidelity \\
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\midrule
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$N$ (parallelism) & 14 Communication \\
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& 15 Fragility \\
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& 16 Multi-tenant \\
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\bottomrule
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\end{tabular}
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\column{0.48\textwidth}
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\begin{tabular}{@{}ll@{}}
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\toprule
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\textbf{Term} & \textbf{Walls} \\
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\midrule
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Peak (hardware) & 1 Compute \\
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& 2 Memory \\
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& 6 Streaming \\
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\midrule
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MFU (efficiency) & 3 Software \\
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& 4 Serving \\
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& 5 Batching \\
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\midrule
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Goodput & 8 Ingestion \\
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& 9 Transformation \\
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& 19 Checkpoint \\
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\bottomrule
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\end{tabular}
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\end{columns}
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\vspace{0.5em}
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\centering
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\footnotesize
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Remaining walls: 7 Tail Latency, 10 Locality, 17 Capital, 18 Sustainability, 20 Safety, 21 Sensitivity, 22 Synthesis\\
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These are \textbf{cross-cutting constraints} and \textbf{meta-analysis tools} that modulate the Iron Law terms.
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\end{frame}
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% --- Slide R.7: 22 Walls at a Glance ---
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\begin{frame}{22 Walls at a Glance}
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\centering
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\scriptsize
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\setlength{\tabcolsep}{3pt}
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\begin{tabular}{@{}rllll@{}}
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\toprule
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\textbf{\#} & \textbf{Wall} & \textbf{Domain} & \textbf{Constraint} & \textbf{Key Equation} \\
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\midrule
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1 & Compute & Node & Peak FLOPS ceiling & $T = \text{OPs} / (\text{Peak} \times \eta)$ \\
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2 & Memory & Node & HBM capacity \& bandwidth & $T = |W| / \text{BW}$ \\
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3 & Software & Node & Peak vs achieved FLOPS & $\eta_{\text{MFU}}$ \\
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4 & Serving & Node & Prefill vs decode regimes & TTFT, ITL \\
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5 & Batching & Node & KV cache fragmentation & PagedAttention \\
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6 & Streaming & Node & Injection BW (wafer-scale) & $T = |W| / \text{BW}_{\text{inj}}$ \\
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7 & Tail Latency & Node & P99 grows near saturation & Erlang-C \\
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\midrule
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8 & Ingestion & Data & Storage I/O supply rate & $\rho = \text{BW}_d / \text{BW}_s$ \\
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9 & Transform & Data & CPU preprocessing rate & $T = BS / C$ \\
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10 & Locality & Data & Bisection bandwidth limit & $\text{BW}_{\text{eff}}$ \\
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\midrule
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11 & Complexity & Algorithm & Chinchilla scaling laws & $C = 6PD$ \\
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12 & Reasoning & Algorithm & Inference-time compute & $T = K \times T_{\text{step}}$ \\
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13 & Fidelity & Algorithm & Compression--accuracy tradeoff & $r = 32/b$ \\
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\midrule
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14 & Communication & Fleet & AllReduce synchronization & Ring AllReduce \\
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15 & Fragility & Fleet & Component failures at scale & $\text{MTBF}/N$ \\
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16 & Multi-tenant & Fleet & Queueing delays in shared clusters & $\rho / [2\mu(1{-}\rho)]$ \\
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\midrule
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17 & Capital & Ops & Total cost of ownership & TCO = CapEx + OpEx \\
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18 & Sustainability & Ops & Carbon \& water footprint & $\text{CO}_2 = E \times \text{PUE} \times \text{CI}$ \\
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19 & Checkpoint & Ops & I/O burst penalty on MFU & $T_w / T_i$ \\
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20 & Safety & Ops & Privacy/fairness overhead & DP-SGD $\sigma \propto 1/\varepsilon$ \\
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\midrule
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21 & Sensitivity & Analysis & Binding constraint ID & $\partial T / \partial x_i$ \\
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22 & Synthesis & Analysis & Inverse Roofline (HW from SLA) & $\text{BW} = |W| / T$ \\
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\bottomrule
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\end{tabular}
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\end{frame}
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% --- Slide R.8: Hardware Zoo ---
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\begin{frame}{The Hardware Zoo: Every Platform in mlsysim}
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\note{[2 min] IMPORTANT FOR ISCA. Linger on this slide. Point out that this
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is NOT an NVIDIA tool. Every vendor represented. Custom silicon can be
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added with a 10-line YAML spec.
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% -- FLEX: [CORE]
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}
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\centering
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\scriptsize
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\setlength{\tabcolsep}{3pt}
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\begin{tabular}{@{}llll@{}}
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\toprule
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\textbf{Vendor} & \textbf{Cloud / Training} & \textbf{Edge / Inference} & \textbf{TinyML} \\
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\midrule
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\textbf{NVIDIA} & A100, H100, H200, B200 & Jetson Orin NX, Orin Nano & --- \\
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\textbf{AMD} & MI250X, MI300X & --- & --- \\
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\textbf{Intel} & Gaudi\,2, Gaudi\,3 & --- & --- \\
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\textbf{Google} & TPU v4, TPU v5e & Coral Edge TPU & --- \\
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\textbf{AWS} & Trainium2 & Inferentia2 & --- \\
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\textbf{Cerebras} & CS-2 (wafer-scale) & --- & --- \\
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\textbf{MCU} & --- & --- & ESP32-S3, nRF52840 \\
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\textbf{Custom} & \multicolumn{3}{l}{Any accelerator via \texttt{mlsysim.Hardware.from\_spec(\{...\})}} \\
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\bottomrule
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\end{tabular}
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\vspace{0.5em}
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\small
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\textbf{5 vendors, 20+ platforms, TinyML to Frontier.}\\[0.2em]
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\alert{mlsysim is a multi-vendor analytical engine, not an NVIDIA wrapper.}
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\end{frame}
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% --- Slide R.9: Try It Now ---
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\begin{frame}[fragile]{Try It Now}
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\begin{center}
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\Large\bfseries
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One install. No GPU. Sub-second answers.
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\end{center}
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\vspace{1em}
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\begin{lstlisting}[language=bash, backgroundcolor=\color{crimson!6}]
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pip install mlsysim
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\end{lstlisting}
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\vspace{0.5em}
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\begin{lstlisting}
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import mlsysim
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# Profile Llama-3 8B on H100 in one line
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profile = mlsysim.Engine.solve(
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mlsysim.Models.Language.Llama3_8B,
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mlsysim.Hardware.H100,
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batch_size=1, seq_len=2048
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)
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print(profile.summary())
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# => bottleneck, latency, throughput, MFU, feasibility
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\end{lstlisting}
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\vspace{0.5em}
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\centering
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\small
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\begin{tabular}{@{}ll@{}}
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GitHub: & \texttt{github.com/harvard-edge/mlsysim} \\
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Docs: & \texttt{mlsysim.readthedocs.io} \\
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Paper: & \texttt{arxiv.org/abs/XXXX.XXXXX} \\
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\end{tabular}
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\end{frame}
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