Systems for verifiable outsourcing incur costs for a prover, a verifier, and precomputation;
outsourcing makes sense when the combination of these costs is cheaper than not …

Sparse tensor algorithms are becoming widespread, particularly in the domains of deep
learning, graph and data analytics, and scientific computing. Current high-performance …

Multicores are now ubiquitous, but programmers still write sequential code. Speculative
parallelization is an enticing approach to parallelize code while retaining the ease of …

Automatic generation of parallel code for general-purpose commodity processors is a
challenging computational problem. Nevertheless, there is a lot of latent thread-level …

Traditional von Neumann GPGPUs only allow threads to communicate through memory on a
group-to-group basis. In this model, a group of producer threads writes intermediate values …

The marketplace for general-purpose microprocessors offers hundreds of functionally similar
models, differing by traits like frequency, core count, cache size, memory bandwidth, and …

Irregular applications are increasingly common in diverse domains, like graph analytics and
sparse linear algebra. Accelerating these applications is challenging because of their …

The design of heterogeneous systems that include domain specific accelerators is a
challenging and time-consuming process. While taking into account area constraints …

Virtual memory, specifically paging, is undergoing significant innovation due to being
challenged by new demands from modern workloads. Recent work has demonstrated an …

The rise of general-purpose computing on GPUs has influenced architectural innovation on
them. The introduction of an on-chip cache hierarchy is one such innovation. High L1 miss …