Modern Workloads Research Articles

Range Translations for Fast Virtual Memory

Modern workloads suffer high execution-time overhead due to page-based virtual memory. The authors introduce range translations that map arbitrary-sized virtual memory ranges to contiguous physical memory pages while retaining the flexibility of paging. A range translation reduces address translation to a range lookup that delivers near zero virtual memory overhead.

Why Does Data Prefetching Not Work for Modern Workloads?

Why Does Data Prefetching Not Work for Modern Workloads?

Redundant memory mappings for fast access to large memories

Page-based virtual memory improves programmer productivity, security, and memory utilization, but incurs performance overheads due to costly page table walks after TLB misses. This overhead can reach 50% for modern workloads that access increasingly vast memory with stagnating TLB sizes. To reduce the overhead of virtual memory, this paper proposes Redundant Memory Mappings (RMM), which leverage ranges of pages and provides an efficient, alternative representation of many virtual-to-physical mappings. We define a range be a subset of process's pages that are virtually and physically contiguous. RMM translates each range with a single range table entry, enabling a modest number of entries to translate most of the process's address space. RMM operates in parallel with standard paging and uses a software range table and hardware range TLB with arbitrarily large reach. We modify the operating system to automatically detect ranges and to increase their likelihood with eager page allocation. RMM is thus transparent to applications. We prototype RMM software in Linux and emulate the hardware. RMM performs substantially better than paging alone and huge pages, and improves a wider variety of workloads than direct segments (one range per program), reducing the overhead of virtual memory to less than 1% on average.

Implications of Executing Compression and Encryption Applications on General Purpose Processors

Compression and encryption applications are important components of modern multimedia workloads. These workloads are typically executed on application specific integrated circuits (ASICs), application specific processors (ASPs), or general purpose processors (GPPs). GPPs are flexible and allow changes in the applications and algorithms better than ASICs and ASPs. However, executing these applications on GPPs is done at a high cost. In this paper, we analyze media compression and encryption applications from the perspective of executing them on a programmable general purpose processor versus ASPs. We select 12 programs from various types of compression and encryption applications. The instruction mix, data types of memory operations, and memory access workloads during the execution of these programs are analyzed. Most of these applications involve processing the data through execution phases (e.g., quantization, encoding, etc.) and temporary results are stored and retrieved between these phases. A metric called overhead memory-access bandwidth per input and output byte is defined to characterize the temporary storage and retrieval of data during execution. We observe that more than 90 percent of the memory accesses made by these programs are temporary data stores and loads arising from the general purpose nature of the execution platform. We also verify the robustness of the proposed metric on different experimental environments using various input data properties and compiler optimizations.