BranchesTags

Latest commit

History

19 Commits

Folders and files

NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md

Repository files navigation

DASP

Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication

Paper

This is the code of our paper published at SC '23:

Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051

Introduction

Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.

Installation

To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.

Execution

Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.

  1. The command 'make xxx' generates an executable file.

make double

make half

  1. Run code on matrix data. Running the program requires one parameter: matrix path.

./spmv_double matrix.mtx

  1. Example

cd test

sh run_double.sh

Contact us

If you have any questions about running the code, please contact Yuechen Lu.

E-mail: yuechenlu@student.cup.edu.cn

About

Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.

Resources

Readme
AGPL-3.0 license
Activity
Custom properties

Stars

29 stars

Watchers

1 watching

Forks

6 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
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");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
})();

Skip to content
BranchesTags
Latest commit
History
19 Commits
Folders and files
NameName
Last commit message
Last commit date
data
data
src
src
test
test
LICENSE
LICENSE
Makefile
Makefile
README.md
README.md
Repository files navigation
DASP
Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication
Paper
This is the code of our paper published at SC '23:
Yuechen Lu and Weifeng Liu. 2023. DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '23). Association for Computing Machinery, New York, NY, USA, Article 73, 1–14. https://doi.org/10.1145/3581784.3607051
Introduction
Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and engineering, graph processing and machine learning applications. In this work, we propose DASP, a new algorithm using specific dense MMA units for accelerating the compute part of general SpMV. We analyze the row-wise distribution of nonzeros and group the rows into three categories containing long, medium and short rows, respectively. We then organize them into small blocks of proper sizes to meet the requirement of MMA computation. For the three categories, DASP offers different strategies to complete SpMV by efficiently utilizing the MMA units.
Installation
To better reproduce experiment results, we suggest an NVIDIA GPU with compute capability 8.0. DASP evaluation requires the CUDA GPU driver, the nvcc CUDA compiler, and the cuSPARSE library, all of them are included with the CUDA Toolkit.
Execution
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.
  1. The command 'make xxx' generates an executable file.
make double
make half
  1. Run code on matrix data. Running the program requires one parameter: matrix path.
./spmv_double matrix.mtx
  1. Example
cd test
sh run_double.sh
Contact us
If you have any questions about running the code, please contact Yuechen Lu.
E-mail: yuechenlu@student.cup.edu.cn
About
Source code of the SC '23 paper: "DASP: Specific Dense Matrix Multiply-Accumulate Units Accelerated General Sparse Matrix-Vector Multiplication" by Yuechen Lu and Weifeng Liu.
Resources
Readme
AGPL-3.0 license
Activity
Custom properties
Stars
29 stars
Watchers
1 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages