Alexander Baumstark
YOU?
Author Swipe
View article: Hybrid Transactional/Analytical Graph Processing in Modern Memory Hierarchies
Hybrid Transactional/Analytical Graph Processing in Modern Memory Hierarchies Open
Today’s enterprise computing architectures are characterized by a complex memory hierarchy: different application requirements in terms of latency, bandwidth, persistence, and access pattern, as well as characteristics of available memory …
View article: So Far and yet so Near - Accelerating Distributed Joins with CXL
So Far and yet so Near - Accelerating Distributed Joins with CXL Open
Distributed partitioned joins are one of the most expensive operators in distributed DBMSs where a major part of the execution is attributed to network transfer costs. Although high-speed network technologies, such as RDMA, can lower this …
View article: Processing-in-Memory for Databases: Query Processing and Data Transfer
Processing-in-Memory for Databases: Query Processing and Data Transfer Open
The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and – in this way – relieving the CPU from processing. Parti…
View article: Adaptive update handling for graph HTAP
Adaptive update handling for graph HTAP Open
Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostl…
View article: Adaptive query compilation in graph databases
Adaptive query compilation in graph databases Open
Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and exploit characteristics of modern hardware. Particularly for graph database queries, which often execute…
View article: Adaptive Query Compilation in Graph Databases
Adaptive Query Compilation in Graph Databases Open
Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and to exploit characteristics of modern hardware. Particularly for graph database queries, which often exec…
View article: Adaptive Update Handling for Graph HTAP
Adaptive Update Handling for Graph HTAP Open
Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostl…
View article: Instant Graph Query Recovery on Persistent Memory
Instant Graph Query Recovery on Persistent Memory Open
Persistent memory (PMem) - also known as non-volatile memory (NVM) - offers new opportunities not only for the design of data structures and system architectures but also for failure recovery in databases. However, instant recovery can mea…