Search for: All records

Fast, byte-addressable persistent memory (PM) is becoming a reality in products. However, porting legacy kernel file systems to fully support PM requires substantial effort and encounters the challenge of bridging the gap between block-based access granularity and byte-addressability. Moreover, new PM-specific file systems remain far from production-ready, preventing them from being widely used. In this paper, we propose P2CACHE, a novel in-kernel caching mechanism to explore how legacy kernel file systems can effectively evolve in the face of fast, byte-addressable PM. P2CACHE exploits a read/write-distinguishable memory hierarchy upon a tiered memory system involving both PM and DRAM. P2CACHE leverages PM to serve all write requests for instant data durability and strong crash consistency while using DRAM to serve most read I/Os for high I/O performance. Further, P2CACHE employs a simple yet effective synchronization model between PM and DRAM by leveraging device-level parallelism. Our evaluation shows that P2CACHE can significantly increase the performance of legacy kernel file systems -- e.g., by 200x for RocksDB on Ext4 -- meanwhile equipping them with instant data durability and strong crash consistency, similar to PM-specialized file systems. 

Fast provisioning of serverless functions is salient for serverless platforms. Though lightweight sandboxes (e.g., containers) enclose only necessary files and libraries, a cold launch still requires up to a few seconds to complete. Such slow provisioning prolongs the response time of serverless functions and negatively impacts users’ experiences. This paper analyzes the main reasons for such slowdown and introduces an effective containerization framework, FlashCube. Instead of building a container from scratch, FlashCube quickly and eff iciently assembles it through a group of pre-created general container parts (e.g., namespaces, cgroups, and language runtimes). In addition, FlashCube’s user-space implementation makes it easily applicable to existing commodity serverless platforms. Our preliminary evaluation demonstrates that FlashCube can quickly provision containerized functions in less than 10 ms (vs. ∼400 ms using Docker containers). 

Contemporary GPUs support multiple kernels to run concurrently on the same streaming multiprocessors (SMs). Recent studies have demonstrated that such concurrent kernel execution (CKE) improves both resource utilization and computational throughput. Most of the prior works focus on partitioning the GPU resources at the cooperative thread array (CTA) level or the warp scheduler level to improve CKE. However, significant performance slowdown and unfairness are observed when latency-sensitive kernels co-run with bandwidth-intensive ones. The reason is that bandwidth over-subscription from bandwidth-intensive kernels leads to much aggravated memory access latency, which is highly detrimental to latency-sensitive kernels. Even among bandwidth-intensive kernels, more intensive kernels may unfairly consume much higher bandwidth than less-intensive ones. In this article, we first make a case that such problems cannot be sufficiently solved by managing CTA combinations alone and reveal the fundamental reasons. Then, we propose a coordinated approach for CTA combination and bandwidth partitioning. Our approach dynamically detects co-running kernels as latency sensitive or bandwidth intensive. As both the DRAM bandwidth and L2-to-L1 Network-on-Chip (NoC) bandwidth can be the critical resource, our approach partitions both bandwidth resources coordinately along with selecting proper CTA combinations. The key objective is to allocate more CTA resources for latency-sensitive kernels and more NoC/DRAM bandwidth resources to NoC-/DRAM-intensive kernels. We achieve it using a variation of dominant resource fairness (DRF). Compared with two state-of-the-art CKE optimization schemes, SMK [52] and WS [55], our approach improves the average harmonic speedup by 78% and 39%, respectively. Even compared to the best possible CTA combinations, which are obtained from an exhaustive search among all possible CTA combinations, our approach improves the harmonic speedup by up to 51% and 11% on average. 

Point-of-care COVID-19 assays that are more sensitive than the current RT-PCR (reverse transcription polymerase chain reaction) gold standard assay are needed to improve disease control efforts. We describe the development of a portable, ultrasensitive saliva-based COVID-19 assay with a 15-min sample-to-answer time that does not require RNA isolation or laboratory equipment. This assay uses CRISPR-Cas12a activity to enhance viral amplicon signal, which is stimulated by the laser diode of a smartphone-based fluorescence microscope device. This device robustly quantified viral load over a broad linear range (1 to 10 5 copies/μl) and exhibited a limit of detection (0.38 copies/μl) below that of the RT-PCR reference assay. CRISPR-read SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) RNA levels were similar in patient saliva and nasal swabs, and viral loads measured by RT-PCR and the smartphone-read CRISPR assay demonstrated good correlation, supporting the potential use of this portable assay for saliva-based point-of-care COVID-19 diagnosis. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.