Black Rock Observatory - Creating Large Scale Science-Based ArtPat Rapp
We built a fully functioning observatory and a hand built telescope so we could share the wonder of the cosmos with thousands of people. Our goal: to get people excited about science, space, and art.
This presentation was given at Rochester Mini Maker Faire in Rochester, New York. November 2014.
Black Rock Observatory - Creating Large Scale Science-Based ArtPat Rapp
We built a fully functioning observatory and a hand built telescope so we could share the wonder of the cosmos with thousands of people. Our goal: to get people excited about science, space, and art.
This presentation was given at Rochester Mini Maker Faire in Rochester, New York. November 2014.
ستقرؤون في هذه الرسالة: ضيافة الولي الصالح، خمسة أسئلة يوم القيامة، كيد الشيطان، متى ظهر التلفزيون؟، التهديد بالدخول إلى جهنم، حلق اللحية حرام، طريقة حمل الجنازة على الأكتاف، أهمية الاختبار الدنيوي. ۔ ۔ ۔هذا الكتاب مهم ومفيد جداً، يزيد من علمك وحسناتك إن شاء الله عزوجل، للقراءة على الانترنت اضغط علی زر القراءة، وللتحميل اضغط علی زر التحميل، وأترك تعليقك عن هذا الكتاب في خانة التعليقات، الرجاء مشاركة هذا الكتاب مع الآخرين لتعم الفائدة
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
ستقرؤون في هذه الرسالة: ضيافة الولي الصالح، خمسة أسئلة يوم القيامة، كيد الشيطان، متى ظهر التلفزيون؟، التهديد بالدخول إلى جهنم، حلق اللحية حرام، طريقة حمل الجنازة على الأكتاف، أهمية الاختبار الدنيوي. ۔ ۔ ۔هذا الكتاب مهم ومفيد جداً، يزيد من علمك وحسناتك إن شاء الله عزوجل، للقراءة على الانترنت اضغط علی زر القراءة، وللتحميل اضغط علی زر التحميل، وأترك تعليقك عن هذا الكتاب في خانة التعليقات، الرجاء مشاركة هذا الكتاب مع الآخرين لتعم الفائدة
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found