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Massively Parallelized Rapidly-Exploring Random Tree

Josh Cohen and Boston Cleek

Abstract

The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.

For more details read Massively Parallelizing the RRT.

Instructions to Run

  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory

    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)

  • to visualize call the visualizer.py script with python3 visualizer.py make sure the rrtout folder is in the same directory

    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph

Results

We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.

File Structure

main.cpp

  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles

rrt.cpp & rrt.hpp

  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality

collision_check.cu & collision_check.h

  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels

About

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
20 stars
Watchers
2 watching
Forks
1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
20 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
About
No description, website, or topics provided.
Resources
Readme
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20 stars
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2 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
20 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
20 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
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var __re = new RegExp('^' + ".*" + '

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
About
No description, website, or topics provided.
Resources
Readme
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Stars
20 stars
Watchers
2 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Massively Parallelized Rapidly-Exploring Random Tree
Josh Cohen and Boston Cleek
Abstract
The goal of this project was to explore the possibilities of utilizing massively parallel processors to parallelize the Rapidly-Exploring Random Trees algorithm. Collision checking was found to be the most significant bottleneck, therefore research efforts were focused on paralellizing this aspect of the algorithm. A naive approach was implemented and analyzed, while the infrastructure for further optimizations were built out and the nature of those optimizations are discussed. It was found that for search spaces in which the obstacle count was greater than 2048 the massively parallel implementation on a GPU outperformed the same algorithm run on a CPU, further it was found that future implementations should work to ensure CUDA threads are performing useful computation with techniques such as binning and thread coarsening.
For more details read Massively Parallelizing the RRT.
Instructions to Run
  • run the rrt executable with ./rrt arg1 ag2make sure you have an rrtout folder in the same directory
    • arg1 - random number seed (default 10)
    • arg2 - number of circles to run with (default 2048)
  • to visualize call the visualizer.py script with python3 visualizer.py make
sure the rrtout folder is in the same directory
    • this script depends on numpy and matplotlib
    • if you executed the rrt on the Wilkenson server or another headless server you will likely have to transfer the generated rrtout folder back to a machine that can visualize the graph
Results
We were able to implement a massively parallelized implementation of the RRT algorithm that demonstrated performance improvements over conventional, CPU implementations when the number of obstacles checked was greater than 2048 as can be seen in the figure below, also included is an example solution computed on the GPU.
File Structure
main.cpp
  • creates an rrt instance
  • seeds the random number generator
  • instantiates number of circles
rrt.cpp & rrt.hpp
  • defines the rrt object
  • calls parallel and serial implementations
  • implements writing data out functionality
collision_check.cu & collision_check.h
  • defines kernel1-3 implementations for CUDA
  • defines wrapper for calling kernels
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