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Class project

Review feedback on project proposals
Review project instructions and rubric on website
Can continue using repository from project proposal
Next step is the serial implementation, focusing on good practices that we've discussed/seen in labs
Serial code should be ready for peer code review by Oct 8
After this week's labs, will have everything you need to benchmark, profile and optimize the serial version of your code.
Check project presentation schedule. If you want to make a swap, find another person/group to swap with, and then
- Click Fork button to create your own repository
- Edit README.md in your repository (can use web interface for small chagnes)
- Commmit change to yoru repo and push (if editting local repo)
- Create Pull Request to merge your change into the class repository

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What are the main causes of thrashing and how does Julia mitigate it? Specifically, how does Juliaâ€™s garbage collection reduce thrashing, if there even is a strong connection?

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Can you demonstrate how to import a julia program into python?

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Some form of tree when:

Elements have a meaningful order
Value fast adding/removing and searching of elements
Value not allocating (much) more memory than necessary
Don't mind taking longer to access individual elements
Willing to spend some time maintaing well-ordered tree
Common in database type applications

Generic tree (not particularly useful)

Unbalanced tree - source Wikimedia

Balanced binary tree

Balanced tree - source Wikimedia

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How to excel at code reviews @ BetterProgramming

The code improves the overall health of the system
Quick code reviews, responses, and feedback
Educate and inspire during the code review
Follow the standards when reviewing code
Resolving code review conflicts
Demo UI changes as a part of code review
Ensure that the code review accompanies all tests
When focused, do not interrupt yourself to do code review
Review everything, and donâ€™t make any assumptions
Review the code with the bigger picture in mind

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Astro 528: High-Performance Scientific Computing for Astrophysics (Fall 2025)

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Week 6

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!(all(sorted_merge .== sorted_quick))

!(all(

.== sorted_quick))

!(all(

.==

))

!(all(

))

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from julia.api import Julia
julia = Julia(compiled_modules=False)
from julia import Base
Base.sind(90)

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If using pure Julia, then garbage collector prevents leaks (at least in theory)
In practice, you can use poor practices that cause it to use lots of memory, e.g.,
- Large/many variables in global/module scope
- Not organizing code into self-contained functions
- Allocating more memory than you really need
- Many small allocations
If you call C, Fortran, Python, R, etc., then memory leaks are possible.
Test your code
@time or @allocated to count number/ammount of allocations. Does it match what you expect?
In ProfileCanvas.jl, can use @profview_allocs to visually find functions/lines that allocate lots of memory (not necessarily a leak).

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"Scratch" can mean different things depending on context:

A separate physical disk or file system that is intended to be used for temporary files.
- E.g., Roar's /storage/scratch/USERID/ provides large storage but autodeletes your files
A portion of memory allocated and reserved for holding scratch data
- E.g., preallocating a workspace to be used for auto-differentiation, integration, factoring a matrix, etc.

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The reading talks about being cache friendlyâ€“ if we were performing a search wouldn't how cache friendly the search is be dependent on the search algorithm? How do we know what type of search algorithm is being used if we didn't write the code ourselves? How would we know how to optimize the structure of our code based on the search algorithm we are using and how the program/computer access memory?

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How severe will thrashing be in a high-level language such as Julia? Should we worry about it immediately or only during optimization?

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Setup

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First, setup PyCall.jl (to call Python from Julia) by running

> julia -e 'import Pkg; Pkg.add("PyCall");'

You only need to do that once (for each system you're running it on).

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Use linked list when:

Likely to insert elements and/or change size often
Don't mind taking longer to access elements (other than beginning/end)
Value not allocating (much) more memory than necessary
Useful for frequent sorting

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Inside Jupyter notebook:

In [1]: %load_ext julia.magic
Initializing Julia runtime. This may take some time...

In [2]: %julia [1 2; 3 4] .+ 1
Out[2]:
array([[2, 3],
       [4, 5]], dtype=int64)
In [3]: arr = [1, 2, 3]

In [4]: %julia $arr .+ 1
Out[4]:
array([2, 3, 4], dtype=int64)

In [5]: %julia sum(py"[x**2 for x in arr]")
Out[5]: 14

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Peer Review Logistics

Access
- I'll send you GitHub ID of peer reviewer(s)
- Make sure reviewer(s) can access your repo
  - If your project repo is private: Settings, Collaborators & Teams, Add people, Read, Add Selection.
  - Or make your project repo public: Settings, Change Visibility, plus multiple multiple steps to confirm.
- Make sure you can access repo to review
If using Jupyter notebooks, make sure to add Markdown version of code for getting feedback.

julia -e 'import Pkg; Pkg.add("Weave"); using Weave; convert_doc("NOTEBOOK_NAME.ipynb","NOTEBOOK_NAME.jmd")'

Review instructions
Provide most feedback via GitHub Issues

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from julia import Main as jl
jl.exp(0)
jl.xs = [1, 2, 3]
jl.eval("sin.(xs)")

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Code Review Best Practices @ Palantir

Purpose

Does this code accomplish the authorâ€™s purpose?
Ask questions.

Implementation

Think about how you would have solved the problem.
Do you see potential for useful abstractions?
Think like an adversary, but be nice about it.
Think about libraries or existing product code.
Does the change follow standard patterns?
Does the change add dependencies?
Think about your reading experience.
Does the code adhere to coding guidelines and code style?
Does this code have TODOs?

Maintainability

Read the tests.
Does this CR introduce the risk of breaking test code, staging stacks, or integrations tests?
Leave feedback on code-level documentation, comments, and commit messages.
Was the external documentation updated?

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Data structures

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ToC on side

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Don't make assumptions cartoon

(Credit: Manu via BetterProgramming.pub)

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Array

Consecutive elements in a 1-d array are stored in consecutive memory locations

Use array when:

Know size at time of creation (or won't need to change size often)
Value fast access to elements (not just the beginning/end)
Value not allocating more memory than memory
Very common for scientific performance sensitive code

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How does memory function from cell-to-cell within a notebook? Is it more efficient to split up code over many cells, or have them operate in the same one? How does this impact runtime and general performance?

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Serial version of Code Rubic

Code performs proposed tasks (1 point)
Comprehensive set of unit tests, at least one integration or regression test (1 point)
Code passes tests (1 point)
Student code uses a version control system effectively (1 point)
Repository includes many regular, small commits (1 point)
Documentation for functionsâ€™ purpose and design (1 point)
Comprehensive set of assertions (1 point)
Variable/function names consistent, distinctive & meaningful (1 point)
Useful & consistent code formatting & style (1 point)
Code is modular, rather than having chunks of same code copied and pasted (1 point)

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Lots of small allocations on the heap
Java (probably the first "major" language to have garbage collection built-in) gave garbage collection a bad reputation because it only allows mutable user-defined types (and passes all objects by pointers), making it quite hard to avoid heap allocation of even very small objects.
Julia (and C#) encourage the use of immutable types
Julia pass variables by reference (so they can pass variables on the stack)
C# passes variables by value by default (so they stay on stack, but often unnecessary stack allocations) and can pass by reference.

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Old Q&As

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Admin Announcements

Labs

No lab this week, so can work on your projects
Still meet this Friday for a group work session

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Learning from others with more experience conducting Code Reviews

Best Practices for Code Review @ Smart Bear

Review fewer than 400 lines of code at a time
Take your time. Inspection rates should under 500 LOC per hour
Do not review for more than 60 minutes at a time
Set goals and capture metrics
Authors should annotate source code before the review
Use checklists
Establish a process for fixing defects
Foster a positive code review culture
Embrace the subconscious implications of peer review
Practice lightweight code reviews

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But be careful... sometimes different algorithms give different results. E.g., whether sorting is stable.

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It's more efficient to split up code into separate functions (regardless of whether they are in the same cell or not).
There might be a very slight latency cost of having lots of cells. But that's unlikley significant unless you are making a really big notebook.

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Garbage collection

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all(x_sorted_default .== x_sorted_insertion) && (all(x_sorted_default .== x_sorted_merge) && all(x_sorted_default .== x_sorted_quick))

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How does one check if the code would cause a memory leak?

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Preping for Code Review

Make it easy for your reviewer

Provide overview of what your code is doing in README.md
Where should they start?
What files should they focus on?
What files should they ignore?
Include an example of how to run/use code

Make it easy for you

As code starts getting larger, then

Move code out of notebooks and into .jl files, as functions mature.
Organize similar functions into .jl files in src directory
Use diretories like examples, test
If/when appropiate add more directories (e.g., data, deps, docs)

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Peer Code Review

Constructive suggestions for improving programming practices (1 point)
Specific, constructive suggestions for improving code readability/documentation (1 point)
Specific, constructive suggestions for improving tests and/or assertions (1 point)
Specific, constructive suggestions for improving code modularity/organization/maintainability (1 point)
Specific, constructive suggestions for improving code efficiency (1 point)
Finding any bugs (if code author confirms) (bonus points?)

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Then from python/Jupyter notebook with Python kernel

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import numpy as np
x = np.array([1.0, 2.0, 3.0])

jl.sum(x)

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Prioritizing Code Review Feedback

Handle conflicts different based on the severity. Credit: Alex Hill

Credit: Alex Hill

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What exactly is "scratch" memory, and what differentiates it from other kinds of memory?

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Read the documentation
Choose the algorithm for your problem (e.g., Description of sorting algorithms)
Consider order of algorithm and whether in-place
Most often, I choose the algorithm that's best fit for my data.
But sometimes I might change the data structure to be a better fit for my algorithm

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Thrasing is a result of programming practices.
When you're a beginning programmer, focus on other things first. Benchmark/profile to find inefficient code and then optimize.
As you gain more experience, you'll start to recognize places where thrashing could occur as you start to write them. In that case, a little planning early on can save work down the road.

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jl.include("my_julia_code.jl")
jl.function_in_my_julia_code(x)

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Hash table (aka dictionary/`Dict`) when:

Elements unlikely to be accessed in any particular order
Value pretty fast access to individual elements
Don't mind allocating significantly more memory than necessary
Useful for scripting, non-performance sensitive code

- source Wikimedia, Jorge Stolfi, CC-BY-SA-3.0

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¨objectid°b27370dcd788a222¥nbpkgŠ¯install_time_nsÏÑ×É¬instantiatedÃ²installed_versionsƒ§PlutoUI¦0.7.71©PlutoTest¥0.2.2²PlutoTeachingTools¥0.4.6°terminal_outputs„ªnbpkg_syncÚ2 [0m[1mResolving...[22m [90m===[39m [32m[1m Installed[22m[39m Hyperscript â”€â”€â”€â”€â”€â”€â”€â”€ v0.0.5 [32m[1m Installed[22m[39m ColorTypes â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.12.1 [32m[1m Installed[22m[39m PlutoUI â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.7.71 [32m[1m Installed[22m[39m PlutoTeachingTools â”€ v0.4.6 [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Project.toml` [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Manifest.toml` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m Waiting for notebook process to start... Done. Starting precompilation... [92m[1mPrecompiling[22m[39m project... §PlutoUIÚ2 [0m[1mResolving...[22m [90m===[39m [32m[1m Installed[22m[39m Hyperscript â”€â”€â”€â”€â”€â”€â”€â”€ v0.0.5 [32m[1m Installed[22m[39m ColorTypes â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.12.1 [32m[1m Installed[22m[39m PlutoUI â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.7.71 [32m[1m Installed[22m[39m PlutoTeachingTools â”€ v0.4.6 [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Project.toml` [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Manifest.toml` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m Waiting for notebook process to start... Done. Starting precompilation... [92m[1mPrecompiling[22m[39m project... ©PlutoTestÚ2 [0m[1mResolving...[22m [90m===[39m [32m[1m Installed[22m[39m Hyperscript â”€â”€â”€â”€â”€â”€â”€â”€ v0.0.5 [32m[1m Installed[22m[39m ColorTypes â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.12.1 [32m[1m Installed[22m[39m PlutoUI â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.7.71 [32m[1m Installed[22m[39m PlutoTeachingTools â”€ v0.4.6 [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Project.toml` [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Manifest.toml` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m Waiting for notebook process to start... Done. Starting precompilation... [92m[1mPrecompiling[22m[39m project... ²PlutoTeachingToolsÚ2 [0m[1mResolving...[22m [90m===[39m [32m[1m Installed[22m[39m Hyperscript â”€â”€â”€â”€â”€â”€â”€â”€ v0.0.5 [32m[1m Installed[22m[39m ColorTypes â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.12.1 [32m[1m Installed[22m[39m PlutoUI â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€ v0.7.71 [32m[1m Installed[22m[39m PlutoTeachingTools â”€ v0.4.6 [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Project.toml` [32m[1m No Changes[22m[39m to `~/.julia/scratchspaces/c3e4b0f8-55cb-11ea-2926-15256bba5781/pkg_envs/env_uwcvdjdwum/Manifest.toml` [0m[1mInstantiating...[22m [90m===[39m [0m[1mPrecompiling...[22m [90m===[39m Waiting for notebook process to start... Done. Starting precompilation... [92m[1mPrecompiling[22m[39m project... §enabledÃ·restart_recommended_msgÀ´restart_required_msgÀbusy_packages¶waiting_for_permissionÂÙ,waiting_for_permission_but_probably_disabledÂ«cell_inputsÞ3Ù$ba0434fc-9c97-470c-a948-7969d016e5a2„§cell_idÙ$ba0434fc-9c97-470c-a948-7969d016e5a2¤codeÚÉmd""" # Class project - Review feedback on project proposals - Review [project instructions and rubric](https://psuastro528.github.io/Fall2025/project/) on website - Can continue using repository from project proposal - Next step is the serial implementation, focusing on good practices that we've discussed/seen in labs - Serial code should be ready for peer code review by **Oct 8** - After this week's labs, will have everything you need to benchmark, profile and optimize the serial version of your code. - Check [project presentation schedule](https://github.com/PsuAstro528/PresentationsSchedule2025). If you want to make a swap, find another person/group to swap with, and then - Click **Fork** button to create your own repository - Edit README.md in your repository (can use web interface for small chagnes) - Commmit change to yoru repo and push (if editting local repo) - Create **Pull Request** to merge your change into the class repository """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$d2cc4026-b2f0-4201-bf3b-c1615bbc9ab7„§cell_idÙ$d2cc4026-b2f0-4201-bf3b-c1615bbc9ab7¤codeÙÇblockquote(md""" What are the main causes of thrashing and how does Julia mitigate it? Specifically, how does Juliaâ€™s garbage collection reduce thrashing, if there even is a strong connection? """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$a4e4c54b-cda4-43af-b9ae-b1f5337f5fd0„§cell_idÙ$a4e4c54b-cda4-43af-b9ae-b1f5337f5fd0¤codeÙTblockquote(md""" Can you demonstrate how to import a julia program into python? """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$6853e432-81b5-4761-9e7a-3c48c9867291„§cell_idÙ$6853e432-81b5-4761-9e7a-3c48c9867291¤codeÚîmd""" ### Some form of **tree** when: - Elements have a meaningful order - Value fast adding/removing and searching of elements - Value not allocating (much) more memory than necessary - Don't mind taking longer to access individual elements - Willing to spend some time maintaing well-ordered tree - Common in database type applications #### Generic tree (not particularly useful) ![Unbalanced tree](https://upload.wikimedia.org/wikipedia/commons/a/a9/Unbalanced_binary_tree.svg) - source [Wikimedia](https://en.wikipedia.org/wiki/File:Unbalanced_binary_tree.svg) #### Balanced binary tree ![Balanced tree](https://upload.wikimedia.org/wikipedia/commons/0/06/AVLtreef.svg) - source [Wikimedia](https://en.wikipedia.org/wiki/File:AVLtreef.svg) """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$9b9e06d6-0566-42f0-93bc-65206e3add02„§cell_idÙ$9b9e06d6-0566-42f0-93bc-65206e3add02¤codeÙNbegin local n = 100 vec_of_pairs = collect(zip(rand(1:10,n), randn(n))) end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$840a2af4-4eab-4857-879d-a9c62b1e27a1„§cell_idÙ$840a2af4-4eab-4857-879d-a9c62b1e27a1¤codeÚ’md""" ## [How to excel at code reviews @ BetterProgramming](https://betterprogramming.pub/13-code-review-standards-inspired-by-google-6b8f99f7fd67) 1. The code improves the overall health of the system 2. Quick code reviews, responses, and feedback 3. **Educate and inspire during the code review** 4. Follow the standards when reviewing code 5. Resolving code review conflicts 6. Demo UI changes as a part of code review 7. Ensure that the code review accompanies all tests 8. **When focused, do not interrupt yourself to do code review** 9. Review everything, and **donâ€™t make any assumptions** 10. **Review the code with the bigger picture in mind** """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$a876b548-3b0e-48d1-98bf-6bf005230e92„§cell_idÙ$a876b548-3b0e-48d1-98bf-6bf005230e92¤codeÙSmd"> Astro 528: High-Performance Scientific Computing for Astrophysics (Fall 2025)"¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$373376f2-3557-42f5-bdeb-9ae70ed3d060„§cell_idÙ$373376f2-3557-42f5-bdeb-9ae70ed3d060¤code³md""" ## Week 6 """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$083af3e8-77ad-4a30-8d80-282a5403b163„§cell_idÙ$083af3e8-77ad-4a30-8d80-282a5403b163¤codeÙ'@test !all(sorted_merge.==sorted_quick)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$abf68ae8-2327-44eb-b0dc-24f66ae4f603„§cell_idÙ$abf68ae8-2327-44eb-b0dc-24f66ae4f603¤codeÙ~md""" ```python from julia.api import Julia julia = Julia(compiled_modules=False) from julia import Base Base.sind(90) ``` """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$76c7abf5-8eb5-4fc0-bca0-7c8098699fc1„§cell_idÙ$76c7abf5-8eb5-4fc0-bca0-7c8098699fc1¤codeÚ¯md""" - If using pure Julia, then garbage collector prevents leaks (at least in theory) - In practice, you can use poor practices that cause it to use lots of memory, e.g., - Large/many variables in global/module scope - Not organizing code into self-contained functions - Allocating more memory than you really need - Many small allocations - If you call C, Fortran, Python, R, etc., then memory leaks are possible. - Test your code - `@time` or `@allocated` to count number/ammount of allocations. Does it match what you expect? - In ProfileCanvas.jl, can use `@profview_allocs` to visually find functions/lines that allocate lots of memory (not necessarily a leak). """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$838815fa-5917-4347-bb3c-bf8300d2d942„§cell_idÙ$838815fa-5917-4347-bb3c-bf8300d2d942¤codeÚ±md""" "Scratch" can mean different things depending on context: - A separate physical disk or file system that is intended to be used for temporary files. - E.g., Roar's `/storage/scratch/USERID/` provides large storage but autodeletes your files - A portion of memory allocated and reserved for holding scratch data - E.g., preallocating a workspace to be used for auto-differentiation, integration, factoring a matrix, etc. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$5797afa6-13ac-457d-aeee-5d4403f61787„§cell_idÙ$5797afa6-13ac-457d-aeee-5d4403f61787¤codeÚ¨blockquote(md""" The reading talks about being cache friendly-- if we were performing a search wouldn't how cache friendly the search is be dependent on the search algorithm? How do we know what type of search algorithm is being used if we didn't write the code ourselves? How would we know how to optimize the structure of our code based on the search algorithm we are using and how the program/computer access memory? """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$a541ae26-c160-4549-af71-c4473f0eefb7„§cell_idÙ$a541ae26-c160-4549-af71-c4473f0eefb7¤codeÙœblockquote(md""" How severe will thrashing be in a high-level language such as Julia? Should we worry about it immediately or only during optimization? """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$c247aa92-2803-402d-b1cf-ebbf819c56ac„§cell_idÙ$c247aa92-2803-402d-b1cf-ebbf819c56ac¤code±md""" # Setup """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$e00f7ffc-16dd-4e6f-ad85-74f6117eb958„§cell_idÙ$e00f7ffc-16dd-4e6f-ad85-74f6117eb958¤codeÙÆmd""" First, setup PyCall.jl (to call Python from Julia) by running ```shell > julia -e 'import Pkg; Pkg.add("PyCall");' ``` You only need to do that once (for each system you're running it on). """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$2ba07b9f-97b0-4ec3-90ae-bdf2b5616370„§cell_idÙ$2ba07b9f-97b0-4ec3-90ae-bdf2b5616370¤codeÚ|md""" ![Linked List](https://upload.wikimedia.org/wikipedia/commons/thumb/6/6d/Singly-linked-list.svg/640px-Singly-linked-list.svg.png) Use **linked list** when: - Likely to insert elements and/or change size often - Don't mind taking longer to access elements (other than beginning/end) - Value not allocating (much) more memory than necessary - Useful for frequent sorting """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$53f5a5ca-201f-4aea-aa4c-fe82ae0249f2„§cell_idÙ$53f5a5ca-201f-4aea-aa4c-fe82ae0249f2¤codeÚimd""" Inside Jupyter notebook: ```python In [1]: %load_ext julia.magic Initializing Julia runtime. This may take some time... In [2]: %julia [1 2; 3 4] .+ 1 Out[2]: array([[2, 3], [4, 5]], dtype=int64) In [3]: arr = [1, 2, 3] In [4]: %julia $arr .+ 1 Out[4]: array([2, 3, 4], dtype=int64) In [5]: %julia sum(py"[x**2 for x in arr]") Out[5]: 14 ``` """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$28d23ac8-ca53-4182-87d7-52fee383f2c5„§cell_idÙ$28d23ac8-ca53-4182-87d7-52fee383f2c5¤codeÚ0md""" ## Peer Review Logistics - Access - I'll send you GitHub ID of peer reviewer(s) - Make sure reviewer(s) can access your repo + If your project repo is private: `Settings`, `Collaborators & Teams`, `Add people`, `Read`, `Add Selection`. + Or make your project repo public: `Settings`, `Change Visibility`, plus multiple multiple steps to confirm. - Make sure you can access repo to review - If using Jupyter notebooks, make sure to add Markdown version of code for getting feedback. ```julia julia -e 'import Pkg; Pkg.add("Weave"); using Weave; convert_doc("NOTEBOOK_NAME.ipynb","NOTEBOOK_NAME.jmd")' ``` - [Review instructions](https://psuastro528.github.io/Fall2025/project/code_reviews/how_to/) - Provide most feedback via [GitHub Issues](https://guides.github.com/features/issues/) """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$626cd1f0-b2d0-48e4-88ee-775e1f037480„§cell_idÙ$626cd1f0-b2d0-48e4-88ee-775e1f037480¤codeÙdmd""" ```python from julia import Main as jl jl.exp(0) jl.xs = [1, 2, 3] jl.eval("sin.(xs)") ``` """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$6a0d5ba5-40d0-445d-a0fa-32fcfbad00c9„§cell_idÙ$6a0d5ba5-40d0-445d-a0fa-32fcfbad00c9¤codeÚmd""" ## [Code Review Best Practices @ Palantir](https://blog.palantir.com/code-review-best-practices-19e02780015f) ### Purpose - Does this code accomplish the authorâ€™s purpose? - **Ask questions.** ### Implementation - **Think about how you would have solved the problem.** - Do you see potential for useful abstractions? - **Think like an adversary, but be nice about it.** - Think about libraries or existing product code. - Does the change follow standard patterns? - **Does the change add dependencies**? - Think about your reading experience. - Does the code adhere to coding guidelines and code style? - Does this code have TODOs? ### Maintainability - **Read the tests**. - Does this CR introduce the risk of breaking test code, staging stacks, or integrations tests? - Leave feedback on code-level documentation, comments, and commit messages. - **Was the external documentation updated?** """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$4cde0527-defc-4901-bb4c-071bc115e102„§cell_idÙ$4cde0527-defc-4901-bb4c-071bc115e102¤codeµmd"# Data structures"¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$0588f080-1da7-4df0-930b-582c065cab75„§cell_idÙ$0588f080-1da7-4df0-930b-582c065cab75¤codeÙ:md"ToC on side $(@bind toc_aside CheckBox(;default=true))"¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$f508c1c8-945b-4b99-8985-5f10946b0239„§cell_idÙ$f508c1c8-945b-4b99-8985-5f10946b0239¤codeÚmd""" ![Don't make assumptions cartoon](https://miro.medium.com/max/560/1*g95FYDe5j9X_9SqEj1tycQ.jpeg) (Credit: [Manu](https://ma.nu/) via [BetterProgramming.pub](https://betterprogramming.pub/13-code-review-standards-inspired-by-google-6b8f99f7fd67)) """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$7b7bd5c8-a8e8-445c-9239-9c7322a81313„§cell_idÙ$7b7bd5c8-a8e8-445c-9239-9c7322a81313¤codeÚcmd""" ## Array - Consecutive elements in a 1-d array are stored in consecutive memory locations Use **array** when: - Know size at time of creation (or won't need to change size often) - Value fast access to elements (not just the beginning/end) - Value not allocating more memory than memory - Very common for scientific performance sensitive code """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$f965c656-6b7b-441d-8111-855ecfe79ca6„§cell_idÙ$f965c656-6b7b-441d-8111-855ecfe79ca6¤codeÙæblockquote(md""" How does memory function from cell-to-cell within a notebook? Is it more efficient to split up code over many cells, or have them operate in the same one? How does this impact runtime and general performance? """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$7a13c062-b641-4894-9288-4a79c1005c49„§cell_idÙ$7a13c062-b641-4894-9288-4a79c1005c49¤codeÚ‰md""" ## Serial version of Code Rubic - Code performs proposed tasks (1 point) - Comprehensive set of unit tests, at least one integration or regression test (1 point) - Code passes tests (1 point) - Student code uses a version control system effectively (1 point) - Repository includes many regular, small commits (1 point) - Documentation for functionsâ€™ purpose and design (1 point) - Comprehensive set of assertions (1 point) - Variable/function names consistent, distinctive & meaningful (1 point) - Useful & consistent code formatting & style (1 point) - Code is modular, rather than having chunks of same code copied and pasted (1 point) """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$442d604f-79a0-473d-a5b1-d90ca303fc2e„§cell_idÙ$442d604f-79a0-473d-a5b1-d90ca303fc2e¤codeÙÝbegin function less_than_custom(a::Tuple,b::Tuple) return a[1]md""" Then from python/Jupyter notebook with Python kernel """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$d2f4bb2d-d222-4c61-9c63-a57bdfef1927„§cell_idÙ$d2f4bb2d-d222-4c61-9c63-a57bdfef1927¤codeÙSmd""" ```python import numpy as np x = np.array([1.0, 2.0, 3.0]) jl.sum(x) ``` """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$531f25b0-2d29-4db1-ab44-b927b6377945„§cell_idÙ$531f25b0-2d29-4db1-ab44-b927b6377945¤codeÙ7begin using PlutoUI, PlutoTest, PlutoTeachingTools end¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$e203823e-3a1f-4812-b2a9-6ab80a811860„§cell_idÙ$e203823e-3a1f-4812-b2a9-6ab80a811860¤codeºsorted_merge, sorted_quick¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÂÙ$9380c6be-90bc-47fa-98b5-92e0e8539110„§cell_idÙ$9380c6be-90bc-47fa-98b5-92e0e8539110¤codeÚ#md""" ## Prioritizing Code Review Feedback ![Handle conflicts different based on the severity. Credit: Alex Hill](https://miro.medium.com/max/560/1*zOvsiXkzqVJ7O8KalHhDZQ.jpeg) - Credit: [Alex Hill](https://betterprogramming.pub/13-code-review-standards-inspired-by-google-6b8f99f7fd67) """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$d8fd8af3-a65d-4230-bb35-e1144e459520„§cell_idÙ$d8fd8af3-a65d-4230-bb35-e1144e459520¤codeÙnblockquote(md""" What exactly is "scratch" memory, and what differentiates it from other kinds of memory? """)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$2097dafd-e457-46b4-b6d6-fdf6f66c6675„§cell_idÙ$2097dafd-e457-46b4-b6d6-fdf6f66c6675¤codeÚ‰md""" - Read the documentation - Choose the algorithm for your problem (e.g., [Description of sorting algorithms](https://docs.julialang.org/en/v1/base/sort/#Base.Sort.InsertionSort)) - Consider order of algorithm and whether *in-place* - Most often, I choose the algorithm that's best fit for my data. - But sometimes I might change the data structure to be a better fit for my algorithm """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$c94864a5-996d-432d-8e14-888991c8e119„§cell_idÙ$c94864a5-996d-432d-8e14-888991c8e119¤codeÙ TableOfContents(aside=toc_aside)¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$603442cb-fdd9-4551-b503-6af0dddc86d7„§cell_idÙ$603442cb-fdd9-4551-b503-6af0dddc86d7¤codeÚ|md""" - Thrasing is a result of programming practices. - When you're a beginning programmer, focus on other things first. Benchmark/profile to find inefficient code and then optimize. - As you gain more experience, you'll start to recognize places where thrashing could occur as you start to write them. In that case, a little planning early on can save work down the road. """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$9e23b40c-e252-438f-a151-0e8c3d0d8271„§cell_idÙ$9e23b40c-e252-438f-a151-0e8c3d0d8271¤codeÙVmd""" ```python jl.include("my_julia_code.jl") jl.function_in_my_julia_code(x) ``` """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃÙ$55ad7c5d-a8b4-4b35-bb0f-7784bc7f51c6„§cell_idÙ$55ad7c5d-a8b4-4b35-bb0f-7784bc7f51c6¤codeÚhmd""" ### **Hash table** (aka dictionary/`Dict`) when: - Elements unlikely to be accessed in any particular order - Value pretty fast access to individual elements - Don't mind allocating significantly more memory than necessary - Useful for scripting, non-performance sensitive code ![Hash table](https://upload.wikimedia.org/wikipedia/commons/thumb/7/7d/Hash_table_3_1_1_0_1_0_0_SP.svg/1024px-Hash_table_3_1_1_0_1_0_0_SP.svg.png) - source [Wikimedia](https://commons.wikimedia.org/wiki/File:Hash_table_3_1_1_0_1_0_0_SP.svg), Jorge Stolfi, [CC-BY-SA-3.0](https://creativecommons.org/licenses/by-sa/3.0/deed.en) """¨metadataƒ©show_logsÃ¨disabledÂ®skip_as_scriptÂ«code_foldedÃ«notebook_idÙ$d80f4b82-bfd9-11f0-2d47-2376ea611b3d«in_temp_dirÂ¨metadata€

Class project

Some form of tree when:

Generic tree (not particularly useful)

Balanced binary tree

How to excel at code reviews @ BetterProgramming

Week 6

Setup

Peer Review Logistics

Code Review Best Practices @ Palantir

Purpose

Implementation

Maintainability

Data structures

Array

Serial version of Code Rubic

Old Q&As

Admin Announcements

Labs

Learning from others with more experience conducting Code Reviews

Best Practices for Code Review @ Smart Bear

Garbage collection

Preping for Code Review

Make it easy for your reviewer

Make it easy for you

Peer Code Review

Prioritizing Code Review Feedback

Hash table (aka dictionary/Dict) when:

Hash table (aka dictionary/`Dict`) when: