There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The PASCAL Visual Object Classes Challenge

IEEE Transactions on Pattern Analysis and Machine Intelligence

In the field of HPC, the current hardware trend is to design multiprocessor architectures featuring heterogeneous technologies such as specialized coprocessors (e.g. Cell/BE) or data-parallel accelerators (e.g. GPUs). Approaching the theoretical performance of these architectures is a complex issue. Indeed, substantial efforts have already been devoted to efficiently offload parts of the computations. However, designing an execution model that unifies all computing units and associated embedded memory remains a main challenge. We therefore designed StarPU, an original runtime system providing a high-level, unified execution model tightly coupled with an expressive data management library. The main goal of StarPU is to provide numerical kernel designers with a convenient way to generate parallel tasks over heterogeneous hardware on the one hand, and easily develop and tune powerful scheduling algorithms on the other hand. We have developed several strategies that can be selected seamlessly at run-time, and we have analyzed their efficiency on several algorithms running simultaneously over multiple cores and a GPU. In addition to substantial improvements regarding execution times, we have obtained consistent superlinear parallelism by actually exploiting the heterogeneous nature of the machine. We eventually show that our dynamic approach competes with the highly optimized MAGMA library and overcomes the limitations of the corresponding static scheduling in a portable way. Copyright © 2010 John Wiley & Sons, Ltd.

/pdf/starpu-a-unified-platform-for-task-scheduling-on-2qdx6n5x2r.pdf

StarPU: a unified platform for task scheduling on heterogeneous multicore architectures

Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-
0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and
Technology (CREST) program of Japan Science and Technology Agency (JST).

/pdf/studying-the-impact-of-the-full-network-embedding-on-2akhs4xui2.pdf

Studying the impact of the Full-Network embedding on multimodal pipelines

Cell Superscalar (CellSs) provides a simple, flexible and easy programming approach for the Cell Broadband Engine (Cell/B.E.) that automatically exploits the inherent concurrency of applications at a function or task level. The CellSs environment is based on a source-to-source compiler that translates annotated C or Fortran code and a runtime library tailored for the Cell/B.E. that orchestrates the concurrent execution of the application. We introduce a technique called bypassing that allows CellSs to perform core-to-core Direct Memory Access (DMA) transfers for generic applications. In this review we concisely summarize the bypassing practice and introduce two improvements: just-in-time renaming and lazy write-back. These extensions come at no additional cost and potentially increase performance by improving the perceived bandwidth of the Element Interconnect Bus (EIB). Experiments on five fundamental linear algebra kernels demonstrate the applicability of these techniques and quantify the benefit that can be reaped. We also present performance results for a first prototype of CellSs with bypassing.

Making the Best of Temporal Locality: Just-in-Time Renaming and Lazy Write-Back on the Cell/B.E

The Density Matrix Renormalization Group (DMRG++) is a condensed matter physics application used to study superconductivity properties of materials. It’s main computations consist of calculating hamiltonian matrix which requires sparse matrix-vector multiplications. This paper presents task-based parallelization and optimization strategies of the Hamiltonian algorithm. The algorithm is implemented as a mini-application in C++ and parallelized with OpenMP. The optimization leverages tasking features, such as dependencies or priorities included in the OpenMP standard 4.5. The code refactoring targets performance as much as programmability. The optimized version achieves a speedup of 8.0\(\times \) with 8 threads and 20.5\(\times \) with 40 threads on a Power9 computing node while reducing the memory consumption to 90 MB with respect to the original code, by adding less than ten OpenMP directives.

/pdf/optimization-of-condensed-matter-physics-application-with-2xrkh5dzma.pdf

Optimization of Condensed Matter Physics Application with OpenMP Tasking Model

Deep neural networks are representation learning techniques. During training, a deep net is capable of generating a descriptive language of unprecedented size and detail in machine learning. Extracting the descriptive language coded within a trained CNN model (in the case of image data), and reusing it for other purposes is a field of interest, as it provides access to the visual descriptors previously learnt by the CNN after processing millions of images, without requiring an expensive training phase. Contributions to this field (commonly known as feature representation transfer or transfer learning) have been purely empirical so far, extracting all CNN features from a single layer close to the output and testing their performance by feeding them to a classifier. This approach has provided consistent results, although its relevance is limited to classification tasks. In a completely different approach, in this paper we statistically measure the discriminative power of every single feature found within a deep CNN, when used for characterizing every class of 11 datasets. We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning. Our results confirm that low and middle level features may behave differently to high level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include, and propose a thresholding approach to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces.

On the Behavior of Convolutional Nets for Feature Extraction

Performance analysis tools help the application users to find bottlenecks that prevent the application to run at full speed in current supercomputers. The level of detail and the accuracy of the performance tools are crucial to completely depict the nature of the bottlenecks. The details exposed do not only depend on the nature of the tools (profile-based or trace-based) but also on the mechanism on which they rely (instrumentation or sampling) to gather information.In this paper we present a mechanism called folding that combines both instrumentation and sampling for trace-based performance analysis tools. The folding mechanism takes advantage of long execution runs and low frequency sampling to finely detail the evolution of the user code with minimal overhead on the application. The reports provided by the folding mechanism are extremely useful to understand the behavior of a region of code at a very low level. We also present a practical study we have done in a in-production scenario with the folding mechanism and show that the results of the folding resembles to high frequency sampling.

Jesús Labarta

Papers

Studying the impact of the Full-Network embedding on multimodal pipelines

Making the Best of Temporal Locality: Just-in-Time Renaming and Lazy Write-Back on the Cell/B.E

Optimization of Condensed Matter Physics Application with OpenMP Tasking Model

On the Behavior of Convolutional Nets for Feature Extraction

Folding: Detailed Analysis with Coarse Sampling