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Trace-based Just-in-Time Type Specialization for Dynamic Languages Andreas Gal  + , Brendan Eich  , Mike Shaver  , David Anderson  , David Mandelin  , Mohammad R. Haghighat $ , Blake Kaplan  , Graydon Hoare  , Boris Zbarsky  , Jason Orendorff  , Jesse Ruderman  , Edwin Smith # , Rick Reitmaier # , Michael Bebenita + , Mason Chang +# , Michael Franz + Mozilla Corporation  f gal,brendan,shaver,danderson,dmandelin,mrbkap,graydon,bz,jorendorff,jruderman g @mozilla.com Adobe Corporation # f edwsmith,rreitmai g @adobe.com Intel Corporation $ f mohammad.r.haghighat g @intel.com University of California, Irvine + f mbebenit,changm,franz g @uci.edu Abstract Dynamic languages such as JavaScript are more difcult to com- pile than statically typed ones. Since no concrete type information is available, traditional compilers need to emit generic code that can handle all possible type combinations at runtime. We present an al- ternative compilation technique for dynamically-typed languages that identies frequently executed loop traces at run-time and then generates machine code on the y that is specialized for the ac- tual dynamic types occurring on each path through the loop. Our method provides cheap inter-procedural type specialization, and an elegant and efcient way of incrementally compiling lazily discov- ered alternative paths through nested loops. We have implemented a dynamic compiler for JavaScript based on our technique and we have measured speedups of 10x and more for certain benchmark programs. Categories and Subject Descriptors D.3.4 [ Programming Lan- guages ]: Processors  Incremental compilers, code generation . General Terms Design, Experimentation, Measurement, Perfor- mance. Keywords JavaScript, just-in-time compilation, trace trees. 1. Introduction Dynamic languages such as JavaScript, Python, and Ruby, are pop- ular since they are expressive, accessible to non-experts, and make deployment as easy as distributing a source le. They are used for small scripts as well as for complex applications. JavaScript, for example, is the de facto standard for client-side web programming Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specic permission and/or a fee. PLDI’09, June 1520, 2009, Dublin, Ireland. Copyright c  2009 ACM 978-1-60558-392-1/09/06. . . $5.00 and is used for the application logic of browser-based productivity applications such as Google Mail, Google Docs and Zimbra Col- laboration Suite. In this domain, in order to provide a uid user experience and enable a new generation of applications, virtual ma- chines must provide a low startup time and high performance. Compilers for statically typed languages rely on type i
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---
name: deft
description: "Use deft first for any simple, direct operation on local files, whatever the type: convert, read, extract, edit, compress, merge, split, compare or inspect. Try it before writing a script or one-liner. If unsure, run deft find; if it says no command does this, use another tool. Not for programming work: to read, write or change source code, skip it."
---

# deft

One local command for file jobs. Prefer it over writing Python or shell scripts.

Syntax: `deft <group> <command> [inputs] [-o output] [--options]`

Groups:
- `pdf`: Merge, split, compress, encrypt, forms, OCR, compare, convert
- `image`: Resize, crop, convert, compress, adjust, montage, OCR
- `video`: Trim, merge, convert, compress, subtitles, GIF, frames (ffmpeg)
- `audio`: Convert, trim, normalize, denoise, cut silence (ffmpeg)
- `doc`: Word/ODT/RTF: to text, Markdown, HTML, PDF; find and replace
- `slides`: PowerPoint: extract text, Markdown outline
- `sheet`: Excel/CSV/ODS tables: SQL query, pivot, merge, split, convert
- `json`: JSON: convert, format, merge, diff, validate
- `toml`: TOML: convert, format, merge, diff, validate
- `xml`: XML: convert, format, merge, diff, validate
- `yaml`: YAML: convert, format, merge, diff, validate
- `md`: Markdown to PDF, HTML, Word, EPUB; table of contents
- `html`: HTML to Markdown, text, Word and PDF
- `epub`: E-books: info, to text, Markdown and PDF
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- `qr`: Create QR codes and barcodes; read QR codes

How to use it:
1. Run `deft <group>` to list commands, or `deft find "<what you want>"` to search. If it says `no command does this`, use another tool.
2. Run `deft <group> <command> --help` for options and examples.
3. `deft convert <in> -o <out.ext>` converts between formats by extension.
4. Results are one line starting with `ok`, `partial` or `error`; add `--json` to parse them. Errors include a `hint:` with the fix.

Inputs are never overwritten; pass `--force` or `--in-place` to allow it. Globs and directories work as inputs; several inputs run in parallel.

If `deft` is not installed, ask the user, then follow https://github.com/firstprayer/deft/blob/main/docs/INSTALL.md

Trace-based Just-in-Time Type Specialization for Dynamic
Languages
Andreas Gal∗+, Brendan Eich∗, Mike Shaver∗, David Anderson∗, David Mandelin∗,
Mohammad R. Haghighat$, Blake Kaplan∗, Graydon Hoare∗, Boris Zbarsky∗, Jason Orendorff∗,
Jesse Ruderman∗, Edwin Smith#, Rick Reitmaier#, Michael Bebenita+, Mason Chang+#, Michael Franz+
Mozilla Corporation∗
{gal,brendan,shaver,danderson,dmandelin,mrbkap,graydon,bz,jorendorff,jruderman}@mozilla.com
Adobe Corporation#
{edwsmith,rreitmai}@adobe.com
Intel Corporation$
{mohammad.r.haghighat}@intel.com
University of California, Irvine+
{mbebenit,changm,franz}@uci.edu
Abstract
Dynamic languages such as JavaScript are more difficult to com-
pile than statically typed ones. Since no concrete type information
isavailable,traditionalcompilersneedtoemitgenericcodethatcan
handle all possible type combinations at runtime. We present an al-
ternative compilation technique for dynamically-typed languages
that identifies frequently executed loop traces at run-time and then
generates machine code on the fly that is specialized for the ac-
tual dynamic types occurring on each path through the loop. Our
method provides cheap inter-procedural type specialization, and an
elegant and efficient way of incrementally compiling lazily discov-
ered alternative paths through nested loops. We have implemented
a dynamic compiler for JavaScript based on our technique and we
have measured speedups of 10x and more for certain benchmark
programs.
Categories and Subject Descriptors D.3.4 [Programming Lan-
guages]: Processors — Incremental compilers, code generation.
General Terms Design, Experimentation, Measurement, Perfor-
mance.
Keywords JavaScript, just-in-time compilation, trace trees.
1. Introduction
Dynamic languages such as JavaScript, Python, and Ruby, are pop-
ular since they are expressive, accessible to non-experts, and make
deployment as easy as distributing a source file. They are used for
small scripts as well as for complex applications. JavaScript, for
example, is the de facto standard for client-side web programming
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. To copy otherwise, to republish, to post on servers or to redistribute
to lists, requires prior specific permission and/or a fee.
PLDI’09, June 15–20, 2009, Dublin, Ireland.
Copyright c© 2009 ACM 978-1-60558-392-1/09/06...$5.00
and is used for the application logic of browser-based productivity
applications such as Google Mail, Google Docs and Zimbra Col-
laboration Suite. In this domain, in order to provide a fluid user
experience and enable a new generation of applications, virtual ma-
chines must provide a low startup time and high performance.
Compilers for statically typed languages rely on type informa-
tion to generate efficien
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