A fast analysis for thread-local garbage collection with dynamic class loading

Long-running, heavily multi-threaded, Java server applications make stringent demands of garbage collector (GC) performance. Synchronisation of all application threads before garbage collection is a significant bottleneck for JVMs that use native threads. We present a new static analysis and a novel GC framework designed to address this issue by allowing independent collection of thread-local heaps. In contrast to previous work, our solution safely classifies objects even in the presence of dynamic class loading, requires neither write-barriers that may do unbounded work, nor synchronisation, nor locks during thread-local collections; our analysis is sufficiently fast to permit its integration into a high-performance, production-quality virtual machine

Fast and Lean Immutable Multi-Maps on the JVM based on Heterogeneous Hash-Array Mapped Tries

An immutable multi-map is a many-to-many thread-friendly map data structure with expected fast insert and lookup operations. This data structure is used for applications processing graphs or many-to-many relations as applied in static analysis of object-oriented systems. When processing such big data sets the memory overhead of the data structure encoding itself is a memory usage bottleneck. Motivated by reuse and type-safety, libraries for Java, Scala and Clojure typically implement immutable multi-maps by nesting sets as the values with the keys of a trie map. Like this, based on our measurements the expected byte overhead for a sparse multi-map per stored entry adds up to around 65B, which renders it unfeasible to compute with effectively on the JVM. In this paper we propose a general framework for Hash-Array Mapped Tries on the JVM which can store type-heterogeneous keys and values: a Heterogeneous Hash-Array Mapped Trie (HHAMT). Among other applications, this allows for a highly efficient multi-map encoding by (a) not reserving space for empty value sets and (b) inlining the values of singleton sets while maintaining a (c) type-safe API. We detail the necessary encoding and optimizations to mitigate the overhead of storing and retrieving heterogeneous data in a hash-trie. Furthermore, we evaluate HHAMT specifically for the application to multi-maps, comparing them to state-of-the-art encodings of multi-maps in Java, Scala and Clojure. We isolate key differences using microbenchmarks and validate the resulting conclusions on a real world case in static analysis. The new encoding brings the per key-value storage overhead down to 30B: a 2x improvement. With additional inlining of primitive values it reaches a 4x improvement

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

Tracking TCRß sequence clonotype expansions during antiviral therapy using high-throughput sequencing of the hypervariable region

To maintain a persistent infection viruses such as hepatitis C virus (HCV) employ a range of mechanisms that subvert protective T cell responses. The suppression of antigen-specific T cell responses by HCV hinders efforts to profile T cell responses during chronic infection and antiviral therapy. Conventional methods of detecting antigen-specific T cells utilize either antigen stimulation (e.g., ELISpot, proliferation assays, cytokine production) or antigen-loaded tetramer staining. This limits the ability to profile T cell responses during chronic infection due to suppressed effector function and the requirement for prior knowledge of antigenic viral peptide sequences. Recently, high-throughput sequencing (HTS) technologies have been developed for the analysis of T cell repertoires. In the present study, we have assessed the feasibility of HTS of the TCRβ complementarity determining region (CDR)3 to track T cell expansions in an antigen-independent manner. Using sequential blood samples from HCV-infected individuals undergoing antiviral therapy, we were able to measure the population frequencies of >35,000 TCRβ sequence clonotypes in each individual over the course of 12 weeks. TRBV/TRBJ gene segment usage varied markedly between individuals but remained relatively constant within individuals across the course of therapy. Despite this stable TRBV/TRBJ gene segment usage, a number of TCRβ sequence clonotypes showed dramatic changes in read frequency. These changes could not be linked to therapy outcomes in the present study; however, the TCRβ CDR3 sequences with the largest fold changes did include sequences with identical TRBV/TRBJ gene segment usage and high junction region homology to previously published CDR3 sequences from HCV-specific T cells targeting the HLA-B*0801-restricted 1395HSKKKCDEL1403 and HLA-A*0101-restricted 1435ATDALMTGY1443 epitopes. The pipeline developed in this proof of concept study provides a platform for the design of future experiments to accurately address the question of whether T cell responses contribute to SVR upon antiviral therapy. This pipeline represents a novel technique to analyze T cell dynamics in situations where conventional antigen-dependent methods are limited due to suppression of T cell functions and highly diverse antigenic sequences

Lifelong behavioral and neuropathological consequences of repetitive mild traumatic brain injury

Objective: Exposure to repetitive concussion, or mild traumatic brain injury (mTBI), has been linked with increased risk of long-term neurodegenerative changes, specifically chronic traumatic encephalopathy (CTE). To date, preclinical studies largely have focused on the immediate aftermath of mTBI, with no literature on the lifelong consequences of mTBI in these models. This study provides the first account of lifelong neurobehavioral and histological consequences of repetitive mTBI providing unique insight into the constellation of evolving and ongoing pathologies with late survival. Methods: Male C57BL/6J mice (aged 2–3 months) were exposed to either single or repetitive mild TBI or sham procedure. Thereafter, animals were monitored and assessed at 24 months post last injury for measures of motor coordination, learning deficits, cognitive function, and anxiety-like behavior prior to euthanasia and preparation of the brains for detailed neuropathological and protein biochemical studies. Results: At 24 months survival animals exposed to r-mTBI showed clear evidence of learning and working memory impairment with a lack of spatial memory and vestibule-motor vestibulomotor deficits compared to sham animals. Associated with these late behavioral deficits there was evidence of ongoing axonal degeneration and neuroinflammation in subcortical white matter tracts. Notably, these changes were also observed after a single mTBI, albeit to a lesser degree than repetitive mTBI. Interpretation: In this context, our current data demonstrate, for the first time, that rather than an acute, time limited event, mild TBI can precipitate a lifelong degenerative process. These data therefore suggest that successful treatment strategies should consider both the acute and chronic nature of mTBI

The emergence of choice: Decision-making and strategic thinking through analogies

Consider the chess game: When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve