This paper explores learnings from first-generation Compute Express Link (CXL) memory expansion to accelerate CXL’s journey to broad, robust use. While broad adoption will be a long journey similar to that of RDMA, we argue that the first step—CXL.mem expansion—is viable on today’s hardware. Through an end-to-end analysis, we revisit common showstoppers: we decompose memory access latency and show that CPU and DRAM internals, rather than the CXL protocol, dominate latency and variability, and we demonstrate how system slack absorbs link error rates above nominal specifications. Along the way, we distill practical guidance on device validation, monitoring, failure modes, security, and multi-tenant interference, and we outline a pragmatic adoption pathway: solidify robust expansion first, prototype micro-pooling next, and move to selective sharing as the ecosystem matures.

In recent years, DRAM technology improvements have scaled at a much slower pace than processors. While server processor core counts grow from 33% to 50% on a yearly cadence, DDR4 memory channel bandwidth has grown at a slower rate, and memory latency has remained relatively flat for some time. Meanwhile, new computing paradigms have emerged, which involve analyzing massive volumes of data in real time and place pressure on the memory subsystem. The combination of these trends makes it important for computer architects to understand the sensitivity of the workload performance to memory bandwidth and latency. In this paper, we outline and validate a methodology for quick and quantitative performance estimation using a real-world workload.

Two recent trends that have emerged include (1) Rapid growth in big data technologies with new types of computing models to handle unstructured data, such as map-reduce and noSQL (2) A growing focus on the memory subsystem for performance and power optimizations, particularly with emerging memory technologies offering different characteristics from conventional DRAM (bandwidths, read/write asymmetries). This paper examines how these trends may intersect by characterizing the memory access patterns of various Hadoop and noSQL big data workloads. Using memory DIMM traces collected using special hardware, we analyze the spatial and temporal reference patterns to bring out several insights related to memory and platform usages, such as memory footprints, read-write ratios, bandwidths, latencies, etc. We develop an analysis methodology to understand how conventional optimizations such as caching, prediction, and prefetching may apply to these workloads, and discuss the implications on software and system design.

This paper proposes a collaborative approach in which applications can provide guidance to the operating system regarding allocation and recycling of physical memory. The operating system incorporates this guidance to decide which physical page should be used to back a particular virtual page. The key intuition behind this approach is that application software, as a generator of memory accesses, is best equipped to inform the operating system about the relative access rates and overlapping patterns of usage of its own address space. It is also capable of steering its own algorithms in order to keep its dynamic memory footprint under check when there is a need to reduce power or to contain the spillover effects from bursts in demand. Application software, working cooperatively with the operating system, can therefore help the latter schedule memory more effectively and efficiently than when the operating system is forced to act alone without such guidance. It is particularly difficult to achieve power efficiency without application guidance since power expended in memory is a function not merely of the intensity with which memory is accessed in time but also how many physical ranks are affected by an application's memory usage.

Mobile systems such as PDAs and cell phones play an increasing role in handling visual contents such as images. Thousands of images can be stored in a mobile system with the advances in storage technology: this creates the need for better organization and retrieval of these images. Content Based Image Retrieval (CBIR) is a method to retrieve images based on their visual contents. In CBIR, images are compared by matching their numerical representations called features; CBIR is computation and memory intensive and consumes significant amounts of energy. This article examines energy conservation for CBIR on mobile systems. We present three improvements to save energy while performing the computation on the mobile system: selective loading, adaptive loading, and caching features in memory. Using these improvements adaptively reduces the features to be loaded into memory for each search. The reduction is achieved by estimating the difficulty of the search. If the images in the collection are dissimilar, fewer features are sufficient; less computation is performed and energy can be saved. We also consider the effect of consecutive user queries and show how features can be cached in memory to save energy. We implement a CBIR algorithm on an HP iPAQ hw6945 and show that these improvements can save energy and allow CBIR to scale up to 50,000 images on a mobile system. We further investigate if energy can be saved by migrating parts of the computation to a server, called computation offloading . We analyze the impact of the wireless bandwidth, server speed, number of indexed images, and the number of image queries on the energy consumption. Using our scheme, CBIR can be made energy efficient under all conditions.

This paper presents a method to allocate resources for real-time tasks using the "Infrastructure as a Service" model offered by cloud computing. Real-time tasks have to be completed before deadlines, and cloud computing offers selection of resources with different speeds and costs. In cloud computing, resource allocations can be scaled up based on the requirements; this is called elasticity and is the key difference from existing multiprocessor task allocation. Scalable resources make economical allocation of resources an important problem. We analyze the problem of allocating resources for a set of realtime tasks such that the economic cost is minimized and all the deadlines are met. We formulate the problem as a constrained optimization problem and propose a polynomial-time solution to allocate resources efficiently. We compare the economic costs and performance provided by our solution with the optimal solution and an EDF (earliest deadline first) method. We show how the cost varies based on the distribution of the tasks.

Mobile robots are widely used for computation-intensive tasks such as surveillance, moving object recognition and tracking. Existing studies perform the computation entirely on robot processors or on dedicated servers. The robot processors are limited by their computation capability; real-time performance may not be achieved. Even though servers can perform tasks faster, the communication time between robots and servers is affected by variations in wireless bandwidths. In this paper, we present a system for realtime moving object recognition and tracking using computation offloading. Offloading migrates computation to servers to reduce the computation time on the robots. However, the migration consumes additional time, referred as communication time in this paper. The communication time is dependent on data size exchanged and the available wireless bandwidth. We estimate the computation and communication needed for the tasks and choose to execute them on robot processors or servers to minimize the total execution time, in order to satisfy real-time constraints.

Offloading can save energy on mobile systems for computation-intensive applications. The mobile systems send programs and data to grid-powered servers where computation is performed. Offloading, however, causes privacy concerns because sensitive data may be sent to servers. This paper investigates how to protect privacy in computation offloading. We use steganography to hide data before sending them to servers. This paper evaluates the tradeoff between energy savings and privacy protection for content-based image retrieval with different steganographic techniques. We implement these methods on a PDA and compare their energy consumption, performance, and effectiveness of protecting privacy.

Multimedia content adaptation has become important due to many devices with different amounts of resources like display sizes, memories, and computation capabilities. Existing studies perform content adaptation on web pages and other media files that have the same start times and durations. In this paper, we present a content adaptation method for multimedia presentations constituting media files with different start times and durations. We perform adaptation based on preferences and temporal constraints specified by authors and generate an order of importance among media files. Our method can automatically generate layouts by computing the locations, start times, and durations of the media files. We compare three solutions to generate layouts: 1) exhaustive search, 2) dynamic programming, and 3) greedy algorithms. We analyze the presentations by varying screen resolutions, media files, preferences, and temporal constraints. Our analysis shows that screen utilizations are 92%, 85%, and 80% for the three methods, respectively. The time to generate layouts for a presentation with 100 media files is 1200, 17, and 10 s, respectively, for the three methods.

Computation offloading can extend the battery lifetime of a portable device by migrating computation to grid- powered servers. The server may charge the portable device for the computation performed, thus providing offloading as a service. The portable device has to trust that the server has indeed performed the computation as claimed. We propose a protocol to establish trust for computation offloading. The protocol uses two keys to establish trust. The first key is part of the input for the computation; executing the program with the first key generates the second key. Without executing the program, it is difficult to generate the correct second key. We implement an algorithm that meets the requirements of the protocol on an HP iPAQ PDA and a server. We demonstrate through experiments how the algorithm establishes trust and quantify the overhead required to establish trust.

The increased availability of electronic devices with multimedia capabilities has resulted in a dramatic growth in the usage of multimedia presentations. In this paper, we present a framework for automatic generation of layouts for multimedia presentations for different screen resolutions. We develop a heuristic to find the layout for a screen of given resolution in polynomial time. The heuristic achieves 92-98% performance compared with the exhaustive search for different screen sizes. The execution time of the heuristic is as low as 15 seconds for a presentation of 100 media files whereas the exhaustive search takes an average of 6 hours.

This paper presents an adaptive compression algorithm for mobile devices to reduce the transmission energy of images through wireless networks. Our algorithm selects different compression schemes based on the amount of details in the images and the available transmission bandwidths. At low bandwidths, significant amounts of energy can be saved because the reduction in transmission energy outweighs the additional processing energy. We implement this adaptive algorithm on an HP iPAQ hw6945 Personal Digital Assistant (PDA). The algorithm is invoked before transmitting images and is transparent to users. The average energy reduction is 20% with an average delay of 1.1 seconds.respectively.

This paper presents a method to save energy for mobile systems that perform content-based image retrieval (CBIR). CBIR is a computation-intensive application and a resource-constrained mobile system may save energy by offloading computation to a grid-powered server. We develop three offloading schemes based on different conditions to save energy for CBIR on mobile systems. We implement a CBIR algorithm on an HP iPAQ hw6945 and physically measure the energy savings.

Offloading may save energy for battery-powered devices by migrating computation to grid-powered servers. Offloading can be provided as a service and the servers charge the devices' users based on the consumed resources. In this paper, we propose a scheme to rank the servers based on the amounts of energy savings. The ranking depends on two factors: (1) the energy saved due to offloading and (2) the energy consumed while waiting for the results. We instrument the offloaded programs to estimate the amounts of computation performed by the servers, and use this information to determine the amounts of saved energy. When the servers perform the offloaded computation, the battery-powered devices wait for the results and consume energy. The ratio of the two factors determines the rank of a server. If a server performs more computation within a shorter duration, the server is ranked higher. We implement our method on an HP iPAQ and demonstrate that our method can effectively rank servers based on energy savings.

We present an adaptive loading scheme to save energy for content based image retrieval (CBIR) in a mobile system. In CBIR, images are represented and compared by high-dimensional vectors called features. Loading these features into memory and comparing them consumes a significant amount of energy. Our method adaptively reduces the features to be loaded into memory for each query image. The reduction is achieved by estimating the difficulty of the query and by reusing cached features in memory for subsequent queries. We implement our method on a PDA and obtain overall energy reduction of 61.3% compared with an existing CBIR implementation.