ACM Transactions on Architecture and Code Optimization

Programmable network data planes have extended the capabilities of packet processing in network devices by allowing custom processing pipelines and agnostic packet processing. While a variety of applications can be implemented on current programmable data ...

For Affine Control Programs or Static Control Programs (SCoP), symbolic counting of reuse distances could induce polynomials for each reuse pair. These polynomials along with cache capacity constraints lead to non-affine (semi-algebraic) sets; and ...

Although approximate computing promises better performance for applications allowing marginal errors, dearth of hardware support and lack of run-time accuracy guarantees makes it difficult to adopt. We present As-Is, an Anytime Speculative Interruptible ...

Centralized power management control systems are hitting a scalability limit. In particular, enforcing a power cap in a many-core system in a performance-friendly manner is quite challenging. Today’s on-chip controller reduces the clock speed of compute ...

Persistent memory (PM) provides large-scale non-volatile memory (NVM) with DRAM-comparable performance. The non-volatility and other unique characteristics of PM architecture bring new opportunities and challenges for the efficient storage system design. ...

GraphBLASis a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are memory bound and may benefit from data locality ...

Intensive communication and synchronization cost for gradients and parameters is the well-known bottleneck of distributed deep learning training. Based on the observations that Synchronous SGD (SSGD) obtains good convergence accuracy while asynchronous ...

Commodity DRAM-based processing-using-memory (PuM) techniques that are supported by off-the-shelf DRAM chips present an opportunity for alleviating the data movement bottleneck at low cost. However, system integration of these techniques imposes non-...

MicroScope and other similar microarchitectural replay attacks take advantage of the characteristics of speculative execution to trap the execution of the victim application in a loop, enabling the attacker to amplify a side-channel attack by executing it ...

The workload co-location, such as deploying offline analysis workloads with online service workloads on the same node, has become common for modern data centers. Workload co-location deployment improves data center resource utilization significantly. ...

Deep neural networks (DNNs) have become key solutions in the natural language processing (NLP) domain. However, the existing accelerators customized for their narrow target models cannot support diverse NLP models. Therefore, naively running complex NLP ...

Convolutional neural networks (CNNs) are emerging as powerful tools for image processing in important commercial applications. We focus on the important problem of improving the latency of image recognition. While CNNs are highly amenable to prefetching ...

With the rapid development of cloud computing, numerous cloud services, containers, and virtual machines have been bringing tremendous demands on high-performance memory resources to modern data centers. Heterogeneous memory, especially the newly released ...

Interpreters are widely used in the implementation of many programming languages, such as Python, Perl, and Java. Even though various JIT compilers emerge in an endless stream, interpretation efficiency still plays a critical role in program performance. ...

In modern processors, speculative execution has significantly improved the performance of processors, but it has also introduced speculative execution vulnerabilities. Recent defenses are based on the delayed execution to block various speculative side ...

This article presents a code generator for sparse tensor contraction computations. It leverages a mathematical representation of loop nest computations in the sparse polyhedral framework (SPF), which extends the polyhedral model to support non-affine ...

Memory efficiency is crucial in training deep learning networks on resource-restricted devices. During backpropagation, forward tensors are used to calculate gradients. Despite the option of keeping those dependencies in memory until they are reused in ...

This article introduces YaConv, a new algorithm to compute convolution using GEMM microkernels from a Basic Linear Algebra Subprograms library that is efficient for multiple CPU architectures. Previous approaches either create a copy of each image element ...

Over the years, processor throughput has steadily increased. However, the memory throughput has not increased at the same rate, which has led to the memory wall problem in turn increasing the gap between effective and theoretical peak processor ...