## Decaffeinated: experiencing the effects of caffeine by its abstinence

“I don’t believe in caffeine!” I found myself repeating that sentence more than once, while hearing colleagues or friends talking and debating about the effects that caffeine has on them.

It is said that caffeine gives addiction, that, in a moderate dosage, it can help the humor and increase the alertness; people drink coffee or other caffeinated beverages when they feel drowsy or when they need to stay up, many affirm that they cannot work or study if they don’t have coffee.

## The Gulf of Naples as seen from Princeton

The Gulf of Naples as seen from Castel dell’Ovo

I’m just back in Princeton, and I decided to share with you this picture taken few days ago during a walk in Naples (Napoli).

I really enjoyed my stay at Home in Italy, having dinner with my family, visiting some relatives, going around in some amazing places, sharing few beers with friends of a lifetime or just met!

But, please, don’t get me wrong: I’m happy to be back to my usual life, skipping dinner alone, “making the science”, playing volleyball with my colleagues,  training MMA, sharing few beers with friends of a lifetime or just met!

Happy birthday to me!

## NECLA’s Annual Volleyball Tournament 2013: the Champions!

The Computing Systems Architecture team, winner of the tournament

## COSMIC: middleware for high performance and reliable multiprocessing on Xeon Phi coprocessors

In Proceedings of the 22nd international symposium on High-performance parallel and distributed computing (HPDC ’13). ACM, New York, NY, USA, 215-226.

It is remarkably easy to offload processing to Intel’s newest manycore coprocessor, the Xeon Phi: it supports a popular ISA (x86-based), a popular OS (Linux) and a popular programming model (OpenMP). Easy portability is attracting programmer efforts to achieve high performance for many applications. But Linux makes it easy for different users to share the Xeon Phi coprocessor, and multiprocessing inefficiencies can easily offset gains made by individual programmers. Our experiments on a production, high-performance Xeon server with multiple Xeon Phi coprocessors show that coprocessor multiprocessing not only slows down the processes but also introduces unreliability (some processes crash unexpectedly).

We propose a new, user-level middleware called COSMIC that improves performance and reliability of multiprocessing on coprocessors like the Xeon Phi. COSMIC seamlessly fits in the existing Xeon Phi software stack and is transparent to programmers. It manages Xeon Phi processes that execute parallel regions offloaded to the coprocessors. Offloads typically have programmer-driven performance directives like thread and affinity requirements. COSMIC does fair scheduling of both processes and offloads, and takes into account conflicting requirements of offloads belonging to different processes. By doing so, it has two benefits. First, it improves multiprocessing performance by preventing thread and memory oversubscription, by avoiding inter-offload interference and by reducing load imbalance on coprocessors and cores. Second, it increases multiprocessing reliability by exploiting programmer-specified per-process coprocessor memory requirements to completely avoid memory oversubscription and crashes. Our experiments on several representative Xeon Phi workloads show that, in a multiprocessing environment, COSMIC improves average core utilization by up to 3 times, reduces make-span by up to 52%, reduces average process latency (turn-around-time) by 70%, and completely eliminates process crashes.

Continue reading the complete paper …

## CRUX PPC 3.0 released!

CRUX PPC 3.0 is now available. Toolchain ships with Graphite support (PPL backend) and also with LTO (Link Time Optimization).
CRUX PPC 3.0 is released as two different archives: 32bit and 64bit. The 32bit version is based on a single lib toolchain instead the 64bit one comes with a multilib toolchain. These two versions share the same ports tree.

## Adding a method for computing Cartesian Product to Groovy’s Collection(s)

In these days I’m using the Groovy programming language very often, I found this language very intuitive and expressive. I try to use, when it is appropriate and convenient , Functional programming style and methods.

One of the key elements of functional programming paradigm (opposite to the imperative paradigm) is “thinking in  space rather than thinking in time”, this translates in a extensive usage of collections and constructs for creating a collection based on existing collections. The most common collection used is the list, the syntactic construct for creating a list based on existing lists is named List comprehension.

I think that the list, or more generic collection, comprehension in Groovy is very powerful (Groovy Collection API), and in my everyday usage I found that it has everything that I need to express the algorithm that I implement in terms of Collection comprehension. By the way, more that once I needed to obtain the Cartesian product of two collections, so I thought it is nice to have a method in Collection for computing the Cartesian product.

### Cartesian product

The Cartesian product is a mathematical operation which returns a set (or product set) from multiple sets. That is, for sets A and B, the Cartesian product A × B is the set of all ordered pairs (a,b) where a ∈ A and b ∈ B:

$f(A, B) = \bigcup_{a\in A}\bigcup_{b\in B} (a, b)$.