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Amazon MemoryDB for Redis is a database service designed for 11 9s of durability with in-memory performance. In this paper, we describe the architecture of MemoryDB and how we leverage open-source Redis, a popular data structure store, to build an ...

This paper presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing ...

When evaluating an iterative graph query over a large graph, systems incur significant overheads due to repeated graph transfer across the memory hierarchy coupled with repeated (redundant) propagation of values over the edges in the graph. An approach ...

Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past decade ...

JPEG is a common encoding format for digital images. Applications that process large numbers of images can be accelerated by decoding multiple images concurrently. We examine the suitability of using a large array of in-memory processors (PIM) to obtain ...

Serverless computing greatly simplifies the use of cloud resources. In particular, Function-as-a-Service (FaaS) platforms enable programmers to develop applications as individual functions that can run and scale independently. Unfortunately, applications ...

Every year more and more advanced approaches to cardinality estimation are published, using learned models or other data and workload specific synopses. In contrast, the majority of commercial in-memory systems still relies on sampling. It is arguably ...

Driven by the increasing requirements of high-performance computing applications, supercomputers are prone to containing more and more computing nodes. Applications running on such a large-scale computing system are likely to spawn millions of ...

Crossbar-based in-memory architectures have emerged as an attractive platform for energy-efficient realization of deep neural networks (DNNs). A key challenge in such architectures is achieving accurate and efficient writes due to the presence of ...

As companies increasingly deploy message-oriented middleware (MOM) systems in mission-critical components of their infrastructures and services, the demand for improved performance and functionality has accelerated the rate at which new systems are ...

This paper introduces the concept of Neural Weight Virtualization - which enables fast and scalable in-memory multitask deep learning on memory-constrained embedded systems. The goal of neural weight virtualization is two-fold: (1) packing multiple DNNs ...

Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or multi-dimensional indexes ...

Serverless computing is an emerging paradigm that greatly simplifies the usage of cloud resources and suits well to many tasks. Most notably, Function-as-a-Service (FaaS) enables programmers to develop cloud applications as individual functions that can ...

Lightweight integer compression algorithms play an important role in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. Thus, there is a large number of algorithms to choose from, while different ...

NeMeSys is a NUMA-aware graph pattern processing engine, which uses the Near Memory Processing paradigm to allow for high scalability. With modern server systems incorporating an increasing amount of main memory, we can store graphs and compute ...

In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been ...

In this paper, we propose a new load balancing scheme which performs data migration or replication according to the loading conditions in heterogeneous distributed in-memory environments. The proposed scheme replicates hot data when the hot data occurs ...

The expressiveness of the vertex-centric programming model introduced by Pregel attracted great attention. Over the years, numerous frameworks emerged, abiding by the same programming model, while relying on widely different architectural designs. The ...

The exploitation of data as well as hardware properties is a core aspect for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, in-memory data processing also ...

Non-volatile random-access memory (NVRAM) is about to hit the market and will require significant changes to the architecture of in-memory database systems. Since such hybrid DRAM-NVRAM database systems will keep the primary data solely persistent in ...