RAPIDSai - Tweet Data Analysis
Tweets Analysis - Keyword: @RAPIDSai
2023年4月5日に公開
概要
Tweets covering
8 days
Latest tweet was on
2023-03-23
Earliest tweet was on
2023-03-14
Total number of tweets analysed
40
Average age of authors' accounts
12 years
要約
The tweets discuss topics related to data science and machine learning, with a focus on the use of GPU acceleration and tools such as the cuSpatial library and RAPIDS.ai. Specific topics include stream-ordered memory operations, generating sparse spatial weights matrices, optimizing performance for data fusion, and using typed dict and parallelization techniques. There are also mentions of specific sessions at the GTC23 conference and discussions about the potential for further optimization in aggregation and join operations. Additionally, there are several suggestions for improving write and read times, such as changing compression methods or streamlining CUDA synchronization.
トピックモデリング
- GPU-based geospatial analytics with cuSpatial library from RAPIDSai
- Accelerating existing systems using Velox and modular composable data system building blocks
- Maximizing CUDA performance through stream-ordered memory operations
- Generating large sparse spatial weights matrix using cuGraph, ApacheArrow, CuPy_Team, numba_jit, and duckdb
- Performance optimization in DataFusion and Parquet compression overhead
感情分析
The tweets express enthusiasm and excitement about the use of @RAPIDSai for accelerating data science and machine learning workflows. They also highlight the innovative and modular building blocks that @VoltronData and other teams are using for scalable data systems. Some tweets also discuss technical challenges and solutions related to compression overhead and optimizing performance. Overall, the language in the tweets conveys a sense of passion and engagement with the field of data science and the use of cutting-edge technology.
トレンド分析
- Use of @RAPIDSai library for GPU geospatial analytics and data science
- Modular composable data system building blocks
- High GPU utilization and scalable data science and machine learning workflows
- Optimization and performance in data processing and analysis
- Discussion and presentation of @RAPIDSai library at #GTC23
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ツイートの種類
Number of Retweets
9
全体の22%
Number of Original tweets
5
全体の12%
Number of tweets that were Quotes
4
全体の10%
Number of tweets that were Replies
25
全体の62%
Number of tweets that contain Hashtags
12
全体の30%
Number of tweets that contain Mentions
40
全体の100%
ツイートに使用されたデバイス
トップ5デバイス
| Source | Count |
|---|---|
| Twitter Web App | 29 |
| Twitter for iPhone | 6 |
| Twitter for Android | 5 |
デバイス分布
フォロワー数上位10アカウント
| Username | Name | Bio | Followers count |
|---|---|---|---|
| NVIDIAAIDev | NVIDIA AI Developer | All things AI for developers from @NVIDIA. Additional developer channels: @NVIDIADeveloper, @NVIDIAHPCDev, and @NVIDIAGameDev. | 37,606 |
| jeffheaton | jeffheaton | YouTuber (75K+ subs), phd computer science, data scientist, and adj faculty at @WUSTL. Any opinions expressed are my own. #InsurTech #FinTech | 10,980 |
| MurrayData | John Murray | Data Scientist & Visiting Professor @geodatascience. Talks about #opendata #AI #deeplearning #geospatial #GPU #HPC #kubernetes #datascience @ApacheArrow #Python | 6,513 |
| TweetAtAKK | Arun Kumar | Assoc Prof at UC San Diego CSE & HDSI. HDSI Faculty Fellow. Research on data management & ML systems. Wisconsin PhD. Freethinker. Poet. Memester. Gay. He/him. | 4,576 |
| datametrician | Josh Patterson | Co-founder and CEO @voltrondata. Originator of @RAPIDSai former @PIFgov (#44). Building bridges not walls. Making Data Science more efficient. | 4,450 |
| harrism | Mark Harris | Software Engineer at NVIDIA. Views expressed are my own, not necessarily NVIDIA's. Software leader; developer; miller; builder; brewer; verber. | 3,941 |
| ayirpelle | priya joseph | geek, entrepreneur, 'I strictly color outside the lines!', opinions r my own indeed. @ayirpelle , universal handle at this time | 3,376 |
| emaxerrno | 🕺💃🤟 Alexander Gallego | Founder & CEO of @RedpandaData - A Kafka® replacement for mission critical systems. 10x Faster; Safe; API compatible. 🇨🇴 | 2,919 |
| andygrove_io | Andy Grove @andygrove@fosstodon.org | @ApacheArrow PMC. Creator of DataFusion & Ballista query engines. Author of "How Query Engines Work" (https://t.co/wW1RM7dYow). GPU-Accelerating Spark @NVIDIA | 1,927 |
| Bradley_Dice | Bradley Dice | GPU-powered data science at @nvidia @RAPIDSAI. PhD from @UMichPhysics @UM_MICDE, @williamjewell alum, @KCMO resident. 🖥️🧪📊🎹 (Views my own.) | 1,188 |
フォロー数上位10アカウント
| Username | Name | Bio | Followers count |
|---|---|---|---|
| MurrayData | John Murray | Data Scientist & Visiting Professor @geodatascience. Talks about #opendata #AI #deeplearning #geospatial #GPU #HPC #kubernetes #datascience @ApacheArrow #Python | 7,147 |
| ayirpelle | priya joseph | geek, entrepreneur, 'I strictly color outside the lines!', opinions r my own indeed. @ayirpelle , universal handle at this time | 5,000 |
| Bradley_Dice | Bradley Dice | GPU-powered data science at @nvidia @RAPIDSAI. PhD from @UMichPhysics @UM_MICDE, @williamjewell alum, @KCMO resident. 🖥️🧪📊🎹 (Views my own.) | 3,152 |
| shoyip | Shoichi Yip @shoyip@mastodon.bida.im | 👨🎓 busy learnin' // currently @SapienzaRoma Physics MSc // @UniTrento Physics BSc | 2,973 |
| emaxerrno | 🕺💃🤟 Alexander Gallego | Founder & CEO of @RedpandaData - A Kafka® replacement for mission critical systems. 10x Faster; Safe; API compatible. 🇨🇴 | 1,660 |
| keithjkraus | Keith Kraus | VP of Engineering and Co-Founder @VoltronData, @RAPIDSAI maintainer, @condaforge core. Previously @NVIDIA. My thoughts are my own. | 1,211 |
| datametrician | Josh Patterson | Co-founder and CEO @voltrondata. Originator of @RAPIDSai former @PIFgov (#44). Building bridges not walls. Making Data Science more efficient. | 994 |
| sardinan_guy | Roberto Panai | I was supposed to be sardinian_guy... | 993 |
| andygrove_io | Andy Grove @andygrove@fosstodon.org | @ApacheArrow PMC. Creator of DataFusion & Ballista query engines. Author of "How Query Engines Work" (https://t.co/wW1RM7dYow). GPU-Accelerating Spark @NVIDIA | 587 |
| miguelusque | Miguel Martínez | Deep Learning Data Scientist at NVIDIA. Challenge accepted! That will be my answer if the challenge is interesting enough. #ViewsAreMyOwn #RTsArentEndorsements | 545 |
最もアクティブなユーザー
| Username | Bio | Number of tweets |
|---|---|---|
| MurrayData | Data Scientist & Visiting Professor @geodatascience. Talks about #opendata #AI #deeplearning #geospatial #GPU #HPC #kubernetes #datascience @ApacheArrow #Python | 14 |
| datametrician | Co-founder and CEO @voltrondata. Originator of @RAPIDSai former @PIFgov (#44). Building bridges not walls. Making Data Science more efficient. | 5 |
| emaxerrno | Founder & CEO of @RedpandaData - A Kafka® replacement for mission critical systems. 10x Faster; Safe; API compatible. 🇨🇴 | 4 |
| Bradley_Dice | GPU-powered data science at @nvidia @RAPIDSAI. PhD from @UMichPhysics @UM_MICDE, @williamjewell alum, @KCMO resident. 🖥️🧪📊🎹 (Views my own.) | 2 |
| andygrove_io | @ApacheArrow PMC. Creator of DataFusion & Ballista query engines. Author of "How Query Engines Work" (https://t.co/wW1RM7dYow). GPU-Accelerating Spark @NVIDIA | 2 |
| harrism | Software Engineer at NVIDIA. Views expressed are my own, not necessarily NVIDIA's. Software leader; developer; miller; builder; brewer; verber. | 2 |
| keithjkraus | VP of Engineering and Co-Founder @VoltronData, @RAPIDSAI maintainer, @condaforge core. Previously @NVIDIA. My thoughts are my own. | 2 |
| NVIDIAAIDev | All things AI for developers from @NVIDIA. Additional developer channels: @NVIDIADeveloper, @NVIDIAHPCDev, and @NVIDIAGameDev. | 1 |
| TweetAtAKK | Assoc Prof at UC San Diego CSE & HDSI. HDSI Faculty Fellow. Research on data management & ML systems. Wisconsin PhD. Freethinker. Poet. Memester. Gay. He/him. | 1 |
| andrewlamb1111 | Database Engineer | 1 |
日別ツイート数
リツイート数上位10ツイート
| ID | Text | Retweet count |
|---|---|---|
| 1636024705248362496 | My colleagues Michael Wang and Thomson Comer will be presenting at #GTC23 about our work on the @RAPIDSai cuSpatial library. Join for a fascinating discussion on GPU geospatial analytics and … | 7 |
| 1638140547167559680 | Day 1 of GTC, a quick overview video of one of my favorite sessions. Accelerating Data Science with @RAPIDSai . #GTC23 @NVIDIAAI https://t.co/r1zpgeqbf7 | 4 |
| 1638142299040366592 | @mim_djo @DataPolars @duckdb For comparison native (non-SQL) solutions in @ApacheArrow & @RAPIDSai #cudf: cudf: 30.2s Arrow: 1m 44s https://t.co/l7TH67POPv | 1 |
| 1636500619891539970 | Great work from @fb_engineering @MetaOpenSource and @VoltronData’s @assignUser @raulcumplido. Velox is a vectorized executor designed to accelerate existing systems (similar to @RAPIDSai). At Voltron Data we love modular composable data … | 1 |
| 1635870689818189828 | Stream-ordered memory operations are essential for maximizing CUDA performance. We achieve high GPU utilization in @RAPIDSai libraries with tools like these, enabling scalable data science and machine learning workflows. Watch … | 1 |
| 1638576450021343236 | @datametrician @MurrayData @mim_djo @DataPolars @duckdb @ApacheArrow @RAPIDSai It might be that `read_parquet` and `sort_values` don't synchronize the CUDA stream, whereas `to_parquet` definitely does. Maybe try adding `rmm._cuda.stream.DEFAULT_STREAM.synchronize()` at the end … | 1 |
| 1638500945498718209 | @MurrayData @mim_djo @DataPolars @duckdb @ApacheArrow @RAPIDSai First read is 2s (incl sort)… that’s amazing! 5x improvement from without GDS. what’s cooler is each gpu can get this perf on the … | 0 |
| 1637454380524871682 | @andygrove_io @emaxerrno @datametrician @fb_engineering @MetaOpenSource @VoltronData @assignUser @raulcumplido @RAPIDSai I think the biggest wins (factor of 2-5) remaining are in aggregation ( distinct and non distinct) and for joins of … | 0 |
| 1635937846551801856 | Generating a large (6tn total) sparse (12bn net) road distance spatial weights matrix for a client. Using @RAPIDSai #cuGraph, @ApacheArrow, @CuPy_Team, @numba_jit #CUDA #jitclass & typed dict, and @duckdb to … | 0 |
| 1635971553773879297 | GB Postcodes graph distance 1km max sparse spatial weights matrix. Points of interest, in this case postcode centroids, are matched to a road node distance table generated with @rapidsai #cuGraph, … | 0 |
いいね数上位10ツイート
| ID | Text | Like count |
|---|---|---|
| 1636024705248362496 | My colleagues Michael Wang and Thomson Comer will be presenting at #GTC23 about our work on the @RAPIDSai cuSpatial library. Join for a fascinating discussion on GPU geospatial analytics and … | 18 |
| 1636500619891539970 | Great work from @fb_engineering @MetaOpenSource and @VoltronData’s @assignUser @raulcumplido. Velox is a vectorized executor designed to accelerate existing systems (similar to @RAPIDSai). At Voltron Data we love modular composable data … | 18 |
| 1638140547167559680 | Day 1 of GTC, a quick overview video of one of my favorite sessions. Accelerating Data Science with @RAPIDSai . #GTC23 @NVIDIAAI https://t.co/r1zpgeqbf7 | 16 |
| 1635870689818189828 | Stream-ordered memory operations are essential for maximizing CUDA performance. We achieve high GPU utilization in @RAPIDSai libraries with tools like these, enabling scalable data science and machine learning workflows. Watch … | 7 |
| 1635937846551801856 | Generating a large (6tn total) sparse (12bn net) road distance spatial weights matrix for a client. Using @RAPIDSai #cuGraph, @ApacheArrow, @CuPy_Team, @numba_jit #CUDA #jitclass & typed dict, and @duckdb to … | 6 |
| 1638473809694019585 | @datametrician @mim_djo @DataPolars @duckdb @ApacheArrow @RAPIDSai Interesting results, Josh, with GDS now working. It's cut the read time in cudf from 10 seconds to 2, but the write (same device) … | 5 |
| 1637454380524871682 | @andygrove_io @emaxerrno @datametrician @fb_engineering @MetaOpenSource @VoltronData @assignUser @raulcumplido @RAPIDSai I think the biggest wins (factor of 2-5) remaining are in aggregation ( distinct and non distinct) and for joins of … | 4 |
| 1638147168560136194 | I'm looking forward to @zstats session on @rapidsai 'Accelerate Data Science in Python with RAPIDS, with Q&A from EMEA Region [S51281a]' at #GTC23 in a few minutes. Link to talk: … | 3 |
| 1635971553773879297 | GB Postcodes graph distance 1km max sparse spatial weights matrix. Points of interest, in this case postcode centroids, are matched to a road node distance table generated with @rapidsai #cuGraph, … | 3 |
| 1636766323161325568 | @datametrician @emaxerrno @fb_engineering @MetaOpenSource @VoltronData @assignUser @raulcumplido @RAPIDSai I agree with this assessment. There is a lot more work to do in DataFusion to get state-of-the-art performance, but given the … | 3 |
使用されている上位言語
トップ10ハッシュタグ
| Hashtag | Count |
|---|---|
| #gtc23 | 7 |
| #cudf | 2 |
| #python | 2 |
| #cugraph | 2 |
| #cuda | 1 |
| #jitclass | 1 |
| #v100 | 1 |
| #gpu | 1 |
| #rapids | 1 |
トップ10メンション
| Mention | Count |
|---|---|
| @rapidsai | 40 |
| @datametrician | 20 |
| @duckdb | 18 |
| @apachearrow | 18 |
| @mim_djo | 17 |
| @datapolars | 17 |
| @fb_engineering | 10 |
| @metaopensource | 10 |
| @voltrondata | 10 |
| @assignuser | 10 |
ツイートのワードクラウド
絵文字分析
ツイートあたりの平均絵文字数:
5
使用された絵文字
| Emoji | Count | Emoji Text |
|---|---|---|
| 👇 | 1 | backhand_index_pointing_down |
| 👀 | 1 | eyes |
絵文字グループ
| Emoji Group | Count |
|---|---|
| People & Body | 2 |
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