Teona Bagashvili

Teona Bagashvili, Tarikul Islam Papon, Subhadeep Sarkar et al. · harvard

Zoned Namespace (ZNS) SSDs offer a new storage model that allows for high throughput and low-latency storage by eliminating device-side garbage collection. The ZNS interface exposes storage as append-only zones, thus enforcing host applications (e.g., database systems) to append, read, and garbage collect their pages. However, the storage abstraction of ZNS SSD hides the substantial differences across different ZNS SSD controller designs, which affects both the performance and predictability of host applications. We find that existing ZNS controllers exhibit (a) increased device-level write amplification (DLWA), (b) increased wear, and (c) increased interference with host I/O. We identify that (i) zone allocation granularity, (ii) zone geometry, (iii) write order, and (iv) zone mapping and management strategy are the four main causes behind this. To provide a predictable storage device, we propose SilentZNS, a new holistic zone management approach that expands the design space of zones and allocates blocks to zones on the fly, while minimizing wear, maintaining parallelism, and avoiding superfluous writes to the device. SilentZNS is a flexible zone allocation scheme that departs from traditional logical-to-physical zone mapping and allows arbitrary collections of blocks to be assigned to a zone. SilentZNS further guarantees wear-leveling and competitive read performance, while substantially reducing DLWA. We implement SilentZNS using the state-of-the-art ConfZNS++ emulator and evaluate it on synthetic microbenchmarks and key-value storage engines. We show that SilentZNS reduces superfluous writes, leading to lower DLWA (92% less at 10% zone occupancy), less overall wear (up to 12%), and up to 3.7x faster workload execution.

Evan Hernandez, Sarah Schwettmann, David Bau et al.

Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope, labeling only a small subset of neurons and behaviors in any network. Is a richer characterization of neuron-level computation possible? We introduce a procedure (called MILAN, for mutual-information-guided linguistic annotation of neurons) that automatically labels neurons with open-ended, compositional, natural language descriptions. Given a neuron, MILAN generates a description by searching for a natural language string that maximizes pointwise mutual information with the image regions in which the neuron is active. MILAN produces fine-grained descriptions that capture categorical, relational, and logical structure in learned features. These descriptions obtain high agreement with human-generated feature descriptions across a diverse set of model architectures and tasks, and can aid in understanding and controlling learned models. We highlight three applications of natural language neuron descriptions. First, we use MILAN for analysis, characterizing the distribution and importance of neurons selective for attribute, category, and relational information in vision models. Second, we use MILAN for auditing, surfacing neurons sensitive to human faces in datasets designed to obscure them. Finally, we use MILAN for editing, improving robustness in an image classifier by deleting neurons sensitive to text features spuriously correlated with class labels.