February 12th, 2008

Talk is Cheap…

RSS icon RSS Category: Personal
Fallback Featured Image

Been busy this month getting talks together.
First up: MSPC08, Workshop on Memory Systems Performance and Correctness.  I got my IWannaBit! paper accepted.  In this paper I propose a single hardware Bit; one that tracks L1 misses & evicts.  Using this one bit I can prove atomicity of a series of operations, or take corrective action if the operations are not atomic.  I basically can convert a hardware stall/fence for memory ordering into a software spin loop (plus I get to do other things while I’m waiting on the hardware).  It’s a pretty cool notion, half-way to Transactional Memory and half-way to DCAS but with almost no hardware (and replaces either LL/SC or CAS ta-boot!).
Next I submitted 4 talks to JavaOne, and amazingly these 3 got accepted:

  • JVM Challenges and Directions in the Multicore Era – This is where I rant against all our current concurrent programming models.  Concurrent programming is very hard, and most of the currently debated solutions (e.g. transactional memory, atomic, locks, threads) does NOT address the hardest part of writing concurrent code.
  • Debugging Data Races – given as a BOF to last years JavaOne.  I’ll freshen up the slides but mostly cover the same ground.
  • Toward a Coding Style for Scalable Nonblocking Data Structures – This the notion behind my NonBlockingHashTable taken to extremes.  Again, these slides  are old but start in the right direction: the JavaOne talk will “up level” the discussion and apply the same techniques to several different datastructures.

I’ll probably be speaking at TSS Las Vegas again this year, but I haven’t a clue about what!     🙂
More cheap talk: I think I’ve figured out how to do all the key bits for a full-blown in-memory DB: atomicity (nobody sees partial updates), non-blocking (no deadlocks; always some transaction can make progress), probably even fair (everybody’s transaction gets to complete eventually, no matter the size or conflicts).  And fast: I bet I can sustain > 100million (simple) DB ops/sec.  Larger transactions will take longer of course.  Of course, it’s all in-memory so the usual notion of durability is limited to the up-time of the server (it’s actually not that bad: I can still checkpoint slowly to disk so e.g. the disk is no more than 60sec out-of-date and is still coherent (represents some snapshot of the real DB)).
Enough Cheap Talk!  I need to get busy making slides…

Leave a Reply

What are we buying today?

Note: this is a guest blog post by Shrinidhi Narasimhan. It’s 2021 and recommendation engines are

July 5, 2021 - by Rohan Rao
The Emergence of Automated Machine Learning in Industry

This post was originally published by K-Tech, Centre of Excellence for Data Science and AI,

June 30, 2021 - by Parul Pandey
What does it take to win a Kaggle competition? Let’s hear it from the winner himself.

In this series of interviews, I present the stories of established Data Scientists and Kaggle

June 14, 2021 - by Parul Pandey
Snowflake on H2O.ai
H2O Integrates with Snowflake Snowpark/Java UDFs: How to better leverage the Snowflake Data Marketplace and deploy In-Database

One of the goals of machine learning is to find unknown predictive features, even hidden

June 9, 2021 - by Eric Gudgion
Getting the best out of H2O.ai’s academic program

“H2O.ai provides impressively scalable implementations of many of the important machine learning tools in a

May 19, 2021 - by Ana Visneski and Jo-Fai Chow
Regístrese para su prueba gratuita y podrá explorar H2O AI Hybrid Cloud

Recientemente, lanzamos nuestra prueba gratuita de 14 días de H2O AI Hybrid Cloud, lo que

May 17, 2021 - by Ana Visneski and Jo-Fai Chow

Start your 14-day free trial today