Active memory controller

Inability to hide main memory latency has been increasingly limiting the performance of modern processors. The problem is worse in large-scale shared memory systems, where remote memory latencies are hundreds, and soon thousands, of processor cycles. To mitigate this problem, we propose an intelligent memory and cache coherence controller (AMC) that can execute Active Memory Operations (AMOs). AMOs are select operations sent to and executed on the home memory controller of data. AMOs can eliminate a significant number of coherence messages, minimize intranode and internode memory traffic, and create opportunities for parallelism. Our implementation of AMOs is cache-coherent and requires no changes to the processor core or DRAM chips. In this paper, we present the microarchitecture design of AMC, and the programming model of AMOs. We compare AMOs\u27 performance to that of several other memory architectures on a variety of scientific and commercial benchmarks. Through simulation, we show that AMOs offer dramatic performance improvements for an important set of data-intensive operations, e.g., up to 50x faster barriers, 12x faster spinlocks, 8.5x-15x faster stream/array operations, and 3x faster database queries. We also present an analytical model that can predict the performance benefits of using AMOs with decent accuracy. The silicon cost required to support AMOs is less than 1% of the die area of a typical high performance processor, based on a standard cell implementation

J-PLUS: The Javalambre Photometric Local Universe Survey

The Javalambre Photometric Local Universe Survey (J-PLUS) is an ongoing 12-band photometric optical survey, observing thousands of square degrees of the Northern Hemisphere from the dedicated JAST/T80 telescope at the Observatorio Astrofisico de Javalambre (OAJ). The T80Cam is a camera with a field of view of 2 deg(2) mounted on a telescope with a diameter of 83 cm, and is equipped with a unique system of filters spanning the entire optical range (3500-10 000 angstrom). This filter system is a combination of broad-, medium-, and narrow-band filters, optimally designed to extract the rest-frame spectral features (the 3700-4000 angstrom Balmer break region, H delta, Ca H+K, the G band, and the Mg b and Ca triplets) that are key to characterizing stellar types and delivering a low-resolution photospectrum for each pixel of the observed sky. With a typical depth of AB similar to 21.25 mag per band, this filter set thus allows for an unbiased and accurate characterization of the stellar population in our Galaxy, it provides an unprecedented 2D photospectral information for all resolved galaxies in the local Universe, as well as accurate photo-z estimates (at the delta z/(1 + z) similar to 0.005-0.03 precision level) for moderately bright (up to r similar to 20 mag) extragalactic sources. While some narrow-band filters are designed for the study of particular emission features ([O II]/lambda 3727, H alpha/lambda 6563) up to z < 0.017, they also provide well-defined windows for the analysis of other emission lines at higher redshifts. As a result, J-PLUS has the potential to contribute to a wide range of fields in Astrophysics, both in the nearby Universe (Milky Way structure, globular clusters, 2D IFU-like studies, stellar populations of nearby and moderate-redshift galaxies, clusters of galaxies) and at high redshifts (emission-line galaxies at z approximate to 0.77, 2.2, and 4.4, quasi-stellar objects, etc.). With this paper, we release the first similar to 1000 deg(2) of J-PLUS data, containing about 4.3 million stars and 3.0 million galaxies at r < 21 mag. With a goal of 8500 deg(2) for the total J-PLUS footprint, these numbers are expected to rise to about 35 million stars and 24 million galaxies by the end of the survey

Autotuning runtime specialization for sparse matrix-vector multiplication

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.TÜBİTAK ; NS

Optimization by runtime specialization for sparse matrix-vector multiplication

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Runtime specialization optimizes programs based on partial information available only at run time. It is applicable when some input data is used repeatedly while other input data varies. This technique has the potential of generating highly efficient codes. In this paper, we explore the potential for obtaining speedups for sparse matrix-dense vector multiplication using runtime specialization, in the case where a single matrix is to be multiplied by many vectors. We experiment with five methods involving runtime specialization, comparing them to methods that do not (including Intel's MKL library). For this work, our focus is the evaluation of the speedups that can be obtained with runtime specialization without considering the overheads of the code generation. Our experiments use 23 matrices from the Matrix Market and Florida collections, and run on five different machines. In 94 of those 115 cases, the specialized code runs faster than any version without specialization. If we only use specialization, the average speedup with respect to Intel's MKL library ranges from 1.44x to 1.77x, depending on the machine. We have also found that the best method depends on the matrix and machine; no method is best for all matrices and machines.NSF ; TÜBİTA

Novel thermo-responsive polyurethane-based hydrogels encapsulating pH-sensitive mesoporous silica nanocarriers

In this work, a novel polyurethane-based hydrogel system encapsulating pH-sensitive nanocarriers has been designed for the triggered release of therapeutic drugs. Two polyurethanes were synthesized to produce functional and temperature-sensitive hydrogels, which were characterized in terms of their physical properties in physiological or pathological aqueous environments, i.e. at pH values of 7.4 and 5. The pH response of hydrogels and release kinetics from encapsulated nanoparticles put in contact with different milieu were also studied

Hybrid Injectable Sol-Gel Systems Based on Thermo-Sensitive Polyurethane Hydrogels Carrying pH-Sensitive Mesoporous Silica Nanoparticles for the Controlled and Triggered Release of Therapeutic Agents

Injectable therapeutic formulations locally releasing their cargo with tunable kinetics in response to external biochemical/physical cues are gaining interest in the scientific community, with the aim to overcome the cons of traditional administration routes. In this work, we proposed an alternative solution to this challenging goal by combining thermo-sensitive hydrogels based on custom-made amphiphilic poly(ether urethane)s (PEUs) and mesoporous silica nanoparticles coated with a self-immolative polymer sensitive to acid pH (MSN-CS-SIP). By exploiting PEU chemical versatility, Boc-protected amino groups were introduced as PEU building block (PEU-Boc), which were then subjected to a deprotection reaction to expose pendant primary amines along the polymer backbone (PEU-NH2, 3E18 -NH2/gPEU\u2013NH2) with the aim to accelerate system response to external acid pH environment. Then, thermo-sensitive hydrogels were designed (15% w/v) showing fast gelation in physiological conditions (approximately 5 min), while no significant changes in gelation temperature and kinetics were induced by the Boc-deprotection. Conversely, free amines in PEU-NH2 effectively enhanced and accelerated acid pH transfer (pH 5) through hydrogel thickness (PEU-Boc and PEU-NH2 gels covered approximately 42 and 52% of the pH delta between their initial pH and the pH of the surrounding buffer within 30 min incubation, respectively). MSN-CS-SIP carrying a fluorescent cargo as model drug (MSN-CS-SIP-Ru) were then encapsulated within the hydrogels with no significant effects on their thermo-sensitivity. Injectability and in situ gelation at 37\ub0C were demonstrated ex vivo through sub-cutaneous injection in rodents. Moreover, MSN-CS-SIP-Ru-loaded gels turned out to be detectable through the skin by IVIS imaging. Cargo acid pH-triggered delivery from PEU-Boc and PEU-NH2 gels was finally demonstrated through drug release tests in neutral and acid pH environments (in acid pH environment approximately 2-fold higher cargo release). Additionally, acid-triggered payload release from PEU-NH2 gels was significantly higher compared to PEU-Boc systems at 3 and 4 days incubation. The herein designed hybrid injectable formulations could thus represent a significant step forward in the development of multi-stimuli sensitive drug carriers. Indeed, being able to adapt their behavior in response to biochemical cues from the surrounding physio-pathological environment, these formulations can effectively trigger the release of their payload according to therapeutic needs