Classic caching algorithms leverage recency, access count, and/or other properties of cached blocks at per-block granularity. However, for media such as flash which have performance and wear penalties for small overwrites, implementing cache policies at a larger granularity is beneficial. Recent research has focused on buffering small blocks and writing in large granularities, sometimes called containers, but it has not explored the ramifications and best strategies for caching compound blocks consisting of logically distinct, but physically co-located, blocks. Containers may have highly diverse blocks, with mixtures of frequently accessed, infrequently accessed, and invalidated blocks. We propose and evaluate Pannier, a flash cache layer that provides high performance while extending flash lifespan. Pannier uses three main techniques: (1) leveraging block access counts to manage cache containers, (2) incorporating block liveness as a property to improve flash cache space efficiency, and (3) designing a multi-step feedback controller to ensure a flash cache reaches its desired lifespan while maintaining performance. Our evaluation shows that Pannier improves flash cache performance and extends lifespan beyond previous per-block and container-aware caching policies. More fundamentally, our investigation highlights the importance of creating new policies for caching compound blocks in flash.

Classic caching algorithms leverage recency, access count, and/or other properties of cached blocks at per-block granularity. However, for media such as flash which have performance and wear penalties for small overwrites, implementing cache policies at a larger granularity is beneficial. Recent research has focused on buffering small blocks and writing in large granularities, called containers, but it has not explored the ramifications and best strategies for caching compound blocks consisting of logically distinct, but physically co-located, blocks. Containers may have highly diverse blocks, with mixtures of frequently accessed, infrequently accessed, and invalidated blocks.

Replicating data off site is critical for disaster recovery reasons, but the current approach of transferring tapes is cumbersome and error prone. Replicating across a wide area network (WAN) is a promising alternative, but fast network connections are expensive or impractical in many remote locations, so improved compression is needed to make WAN replication truly practical. We present a new technique for replicating backup datasets across a WAN that not only eliminates duplicate regions of files (deduplication) but also compresses similar regions of files with delta compression, which is available as a feature of EMC Data Domain systems. Our main contribution is an architecture that adds stream-informed delta compression to already existing deduplication systems and eliminates the need for new, persistent indexes. Unlike techniques based on knowing a file's version or that use a memory cache, our approach achieves delta compression across all data replicated to a server at any time in the past. From a detailed analysis of datasets and statistics from hundreds of customers using our product, we achieve an additional 2X compression from delta compression beyond deduplication and local compression, which enables customers to replicate data that would otherwise fail to complete within their backup window.

A challenge in data analysis and visualization is to build new-generation software tools and systems to truly accelerate scientific discoveries. The recent focus of Princeton's next-generation software project is to investigate how to develop new-generation data analysis and visualization capabilities for genomic scientists to analyze high-throughput genomic datasets. This paper describes the software tools we have recently developed to enable dynamic, large-scale data analysis and visualization of multiple datasets on large-scale, high-resolution display wall systems. Our initial experience with the deployed tools at Princeton's Lewis-Sigler Institute for Integrative Genomics is very encouraging. Scientists can effectively learn new knowledge from multiple datasets, find new insights, and generate new hypotheses that are not possible with current methods.

Genomics is an important emerging scientific field that relies on meaningful data visualization as a key step in analysis. Specifically, most investigation of gene expression microarray data is performed using visualization techniques. However, as microarrays become more ubiquitous, researchers must analyze their own data within the context of previously published work in order to gain a more complete understanding. No current method for microarray visualization and analysis enables biology researchers to observe the greater context of data that surrounds their own results, which severely limits the ability of researchers draw novel conclusions. Here we present a system, called HIDRA, that visually integrates the simultaneous display of multiple microarray datasets to identify important parallels and dissimilarities. We demonstrate the power of our approach through examples of real-world biological insights that can be observed using HIDRA that are not apparent using other techniques.

This paper presents a vision-based geometric alignment system for aligning the projectors in an arbitrarily large display wall. Existing algorithms typically rely on a single camera view and degrade in accuracy as the display resolution exceeds the camera resolution by several orders of magnitude. Naive approaches to integrating multiple zoomed camera views fail since small errors in aligning adjacent views propagate quickly over the display surface to create glaring discontinuities. Our algorithm builds and refines a camera homography tree to automatically register any number of uncalibrated camera images; the resulting system is both faster and significantly more accurate than competing approaches, reliably achieving alignment errors of 0.55 pixels on a 24-projector display in under 9 minutes. Detailed experiments compare our system to two recent display wall alignment algorithms, both on our 18 Megapixel display wall and in simulation. These results indicate that our approach achieves sub-pixel accuracy even on displays with hundreds of projectors.

An inexpensive way to construct a scalable display wall system is to use a cluster of PCs with commodity graphics accelerators to drive an array of projectors. A challenge is to bring off-the-shelf sequential applications to run on such a display wall efficiently without using expensive, high-performance interconnects. We study two execution models for a scalable display wall system: master-slave and synchronized execution models. We have designed and implemented four software tools, two for each execution model, including VDD (Virtual Display Driver), GLP (GL-DLL Replacement), SSE (System-level Synchronized Execution), and ASE (Application-level Synchronized Execution). In order to support the synchronized execution model, we have also designed a broadcast, speculative file cache to provide scalable I/O performance. We report our experimental results with several 3D applications on the display wall to understand the performance implications and tradeoffs of these methods.