Pakettiprosessointijärjestelmien Suorituskykyanalyysi

This thesis investigates the use of measurement, simulation, and modeling methods for the performance analysis of packet processing systems, and more precisely hardware accelerated multiprocessor system-on-chip (MPSoC) devices running task-parallel applications. To guarantee the tight latency and throughput requirements, the devices often incorporate complex hardware accelerated packet scheduling mechanisms. At the same time, due to the complexity of these systems, different software abstractions, such as task-based programming models, are used to develop packet processing applications. These challenges, together with dynamic characteristics of the packet streams makes the performance analysis of packet processing systems non-trivial. We demonstrate that, with extended queue disciplines and support for modeling parallelism, resource network methodology is a viable approach for modeling complex MPSoC based systems running task-based parallel applications on dynamic workloads. The main contributions of our work are three-fold. First, we have extended the toolset of an existing in-house modeling and simulation software, Performance Simulation Environment. The extensions enable modeling of user-definable queue disciplines, which further enable flexible modeling of complex hardware interactions of MPSoCs and the parallelism of task-based programming models. Secondly, we have studied, instrumented, and measured the characteristics of a packet processing system. Finally we have modeled a multi-blade packet processing system with customizable workload and task-parallel application models, and run simulation experiments. In both experiments, the model acts as expected. According to the experiment results, the resource network concept seems to be a viable tool for the performance analysis of packet processing systems. The chosen abstraction level provides desired balance between the functionality, ease of use, and simulation performance.Tässä työssä tutkitaan mittaus-, mallinnus-, ja simulaatiometodien käyttöä pakettiprosessisysteemien, tarkemmin ottaen tehtävärinnakkaisia sovelluksia ajavien laitteistokiihdytettyjen moniydinjärjestelmien, suorityskykyanalyysiin. Tiukoista viive- ja läpivirtausvaatimuksista johtue pakettiprosessointilaitteistot sisältävät usein monimutkaisia laitteistokiihdytettyjä pakettiajoitusmekanismeja. Laittestojen monimutkaisuudesta johtuen pakettiprosessointisovellusten kehittämiseen käytetään usein erilaisia ohjelmointiabstraktioita, kuten tehtävärinnakkaisia ohjelmointimalleja. Laitteston ja ohjelmiston asettamat haasteet yhdessä pakettivirtojen dynaamisen luonteen kanssa tekevät pakettiprosessointijärjestelmien suorituskykyanalyysista epätriviaalia. Työssä havainnollistamme, että laajennettujen jonokurien ja rinnakkaismallinnustuen avulla resurssiverkkometodologia on toimiva lähestymistapa tehtävärinnakkaisia rinnakkaisohjelmointisovelluksia ajavien monimutkaisten laitteistokiihdytettyjen moniydinjärjestelmien suorituskykyanalyysiin dynaamisilla työkuormilla. Työmme päätulokset ovat kolmiosaiset. Ensinnäkin, olemme laajentaneet olemassaolevan mallinnus- ja simulaatioohjelmiston, Performance Simulation Environmentin, ohjelmointityökaluja. Laajennukset mahdollistavat käyttäjän määriteltävien jonokurien mallintamisen, mikä edelleen mahdollistaa tehtävärinnakkaisia sovelluksia ajavien laittestokiihdytettyjen moniydinjärjestelmien laittestovuorovaikutusten joustavan mallinnuksen. Toiseksi, olemme tutkineet ja mitanneet erään pakettiprosessointijärjestelmän ominaisuuksia. Viimeiseksi, olemme mallintaneet pakettiprosessointijärjestelmän muunnettavilla työkuormilla ja tehtävärinnakkaisilla sovellusmalleilla, sekä suorittaneet näitä simulaatiokokein. Molempien kokeiden mallit käyttäytyvät odotetulla tavalla. Koetulosten perusteella resurssiverkkokonsepti vaikuttaa toimivalta työkalulta kompleksien pakettiprosessointijärjestelmien suorituskykyanalyysiin. Valittu abstraktiotaso tarjoaa toivotun tasapainon simulaation suorituskyvyn, toiminnallisuuden ja helppokäyttöisyyden välillä

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent's task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods.Comment: Published at the International Conference on Machine Learning (ICML) 202

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner - well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards. Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite significant unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way. Indeed, even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website.Comment: Project website: https://sites.google.com/view/op3-socce

Greenland Geothermal Heat Flow Database and Map (Version 1)

We compile and analyze all available geothermal heat flow measurements collected in and around Greenland into a new database of 419 sites and generate an accompanying spatial map. This database includes 290 sites previously reported by the International Heat Flow Commission (IHFC), for which we now standardize measurement and metadata quality. This database also includes 129 new sites, which have not been previously reported by the IHFC. These new sites consist of 88 offshore measurements and 41 onshore measurements, of which 24 are subglacial. We employ machine learning to synthesize these in situ measurements into a gridded geothermal heat flow model that is consistent across both continental and marine areas in and around Greenland. This model has a native horizontal resolution of 55ĝ€¯km. In comparison to five existing Greenland geothermal heat flow models, our model has the lowest mean geothermal heat flow for Greenland onshore areas. Our modeled heat flow in central North Greenland is highly sensitive to whether the NGRIP (North GReenland Ice core Project) elevated heat flow anomaly is included in the training dataset. Our model's most distinctive spatial feature is pronounced low geothermal heat flow (<ĝ€¯40ĝ€¯mWĝ€¯m-2) across the North Atlantic Craton of southern Greenland. Crucially, our model does not show an area of elevated heat flow that might be interpreted as remnant from the Icelandic plume track. Finally, we discuss the substantial influence of paleoclimatic and other corrections on geothermal heat flow measurements in Greenland. The in situ measurement database and gridded heat flow model, as well as other supporting materials, are freely available from the GEUS Dataverse (10.22008/FK2/F9P03L; Colgan and Wansing, 2021).publishedVersionPeer reviewe

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10−8) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10−8). The top IBC association for SBP was rs2012318 (P= 6.4 × 10−6) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10−6) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexit

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

To dissect the genetic architecture of blood pressure and assess effects on target-organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure loci, of which 17 were novel and 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target-organ damage in multiple tissues, with minor effects in the kidney. Our findings expand current knowledge of blood pressure pathways and highlight tissues beyond the classic renal system in blood pressure regulation

Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1

Abdominal aortic aneurysm (AAA) is a common cause of morbidity and mortality and has a significant heritability. We carried out a genome-wide association discovery study of 1866 patients with AAA and 5435 controls and replication of promising signals (lead SNP with a p value &lt; 1 × 10-5) in 2871 additional cases and 32,687 controls and performed further follow-up in 1491 AAA and 11,060 controls. In the discovery study, nine loci demonstrated association with AAA (p &lt; 1 × 10-5). In the replication sample, the lead SNP at one of these loci, rs1466535, located within intron 1 of low-density-lipoprotein receptor-related protein 1 (LRP1) demonstrated significant association (p = 0.0042). We confirmed the association of rs1466535 and AAA in our follow-up study (p = 0.035). In a combined analysis (6228 AAA and 49182 controls), rs1466535 had a consistent effect size and direction in all sample sets (combined p = 4.52 × 10-10, odds ratio 1.15 [1.10-1.21]). No associations were seen for either rs1466535 or the 12q13.3 locus in independent association studies of coronary artery disease, blood pressure, diabetes, or hyperlipidaemia, suggesting that this locus is specific to AAA. Gene-expression studies demonstrated a trend toward increased LRP1 expression for the rs1466535 CC genotype in arterial tissues; there was a significant (p = 0.029) 1.19-fold (1.04-1.36) increase in LRP1 expression in CC homozygotes compared to TT homozygotes in aortic adventitia. Functional studies demonstrated that rs1466535 might alter a SREBP-1 binding site and influence enhancer activity at the locus. In conclusion, this study has identified a biologically plausible genetic variant associated specifically with AAA, and we suggest that this variant has a possible functional role in LRP1 expression