@Body

Hi all, just wanted to share a small project I built over a few robotics series. This one builds on top of the mycobot 280pi that I won from a competition, thought it was a fun project to learn with MoveIt2. The raspi itself ran extremely slow, so I had to find an alternative to control from another PC to run the MoveIt and Computer Vision applications via TCP. Feel free to check it out. submitted by /u/OkThought8642 [link] [Kommentare]

Serious question. Iran war is over. We beat them “hugely” according to our POTUS. Putin seems to also be looking for an exit for his war that he also started. Yes, it does seem like the war has cause a massive ripple effect on the entire global economy, leaving the poorest countries to suffer the most. But we just had a first of firsts with an actual crypto sponsored UFC fight on the White house lawn!!! And the fighters are being paid in crypto. What’s more, is we have basically bottomed out over the last few months. So the only scenario I can really see is banana tits to the moon for all the major coins. submitted by /u/OpinionsRdumb [link] [Kommentare]

Derek Jones

A giant shared pixel canvas for the World Cup — paint a pixel for your team, and every pixel is a donation to charity.

Skill demands have shifted toward hybrid, AI-informed roles that IT leaders view as better filled by upskilling insiders than chasing talent in a volatile market.

Source-controlled AI checks on every pull request. Standards as checks, enforced by AI, decided by humans.

Accelerating SQL query execution with GPUs is a central focus in database research. While prior systems have achieved notable speedups by offloading relational operators, the acceleration of the wide range of scalar functions that are supported by analytical engines remains unaddressed. Our analysis reveals that many scalar functions incur substantial computational overhead and often constitute the primary bottleneck in analytical queries on CPUs. Thisobservation motivates a systematic exploration of the opportunities and challenges in accelerating scalar functions on GPUs.Unlike relational operators, which are few in number and standardized, production databases support hundreds of scalar functions. The absence of a standardized specification, combined with this diversity, renders manual GPU porting infeasible. To address this, we present an LLVM-MLIR-based compiler toolchain that automatically translates the CPU-based implementations of scalar functions from production databases into efficient GPU kernels, while preserving their original semantics. Our approach lifts scalar functions to a high-level intermediate representation, applies resource-optimizing transformations, and generates GPU assembly code, supporting all relevant data types, parameters, and database context variables.As existing benchmarks do not sufficiently stress test scalar functions in analytical queries, we introduce a variant of TPC-H that utilizes scalar functions while preserving the original query intent. Integrating our GPU kernels into a state-of-the-art GPU data base system, we demonstrate substantial performance gains over a leading CPU database that uses slightly more expensive hardware: 7.6× on enhanced TPC-H and 6.4× on production queries, furtherwidening the gap between GPU and CPU databases. The generated kernels deliver performance comparable to hand-optimized GPU implementations, establishing our approach as a scalable and practical solution for accelerating scalar functions on GPUs.