According to the October 2018 survey, the most common mobile shopping activities are comparing prices (36%), gathering product information (30%), checking product reviews (30%), paying online (26%) and checking inventory availability (25%).
Mono Solutions Webinar - The Engagement Challenge: Simple Steps for Improving...Localogy
The Local Search Association and Mono Solutions recently published a white paper called “The Engagement Challenge” that documents how SMBs are failing to fully engage with the cloud tools they are purchasing. Product complexity and lack of training (and in some cases, both) are leading to customer churn and product abandonment. This webinar explores the data while examining the cause and how you can better engage with your users to ensure mutual success in the long run.
Watch the webinar: https://youtu.be/Lxhk6yQ2S10
Download the report: https://www.thelsa.org/lsa/the-engagement-challenge.aspx
Mono Solutions Webinar - The Engagement Challenge: Simple Steps for Improving...Localogy
The Local Search Association and Mono Solutions recently published a white paper called “The Engagement Challenge” that documents how SMBs are failing to fully engage with the cloud tools they are purchasing. Product complexity and lack of training (and in some cases, both) are leading to customer churn and product abandonment. This webinar explores the data while examining the cause and how you can better engage with your users to ensure mutual success in the long run.
Watch the webinar: https://youtu.be/Lxhk6yQ2S10
Download the report: https://www.thelsa.org/lsa/the-engagement-challenge.aspx
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).
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).