TAZeR: TAZeR (Transparent Asynchronous Zero-copy Remote I/O) is a remote I/O framework for transparently minimizing the access latencies of remote I/O in workflows. TAZeR captures dynamic and irregular inter-task locality, both temporal and spatial, via adaptive hierarchical staging that ensures most frequently accessed data is `close'.

BigFlowSim: BigFlowSim is a workflow I/O simulator-emulator and trace generator that captures several parameters that affect local and remote I/O performance. BigFlowSim generates a large variety of flows within and between tasks of distributed workflows. The BigFlowSim Driver is helpful for conducting experiments.

MassiveGNN: Graph Neural Networks (GNN) based on massively connected (distributed) GNNs pose significant challenges as even with the best methods, GNN training usually suffers from communication bottlenecks and load imbalance. MassiveGNN introduces performant and productive training for massively connected (distributed) GNNs within the state-of-the-art Amazon DistDGL (distributed Deep Graph Library). It brings practical trade-offs for improving the sampling and communication overheads for representation learning on distributed graphs by developing a parameterized continuous prefetch and eviction scheme.

SamIAm: (Demo) Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics -- where are boundaries located? what segments are logically similar? -- change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we define semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. We apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations.

MemGaze: MemGaze is a memory analysis toolset that combines high-resolution trace analysis and low overhead measurement, both with respect to time and space.

Palm: Palm is a suite of performance modeling tools (Palm, Palm-Task, Representative-Paths, Palm/FastFootprints, MIAMI-NW) to assist performance analysis and predictive model generation. Palm generates models by combining top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. Palm has been used to model irregular applications with sparse data structures and unpredictable access patterns. Recent additions focus on rapid characterization of memory behavior.

QuaL²M (QuaLM): Quantitative Learned Latency Model [Extra datasets]

Scientific workflows: A suite of distributed scientific workflows with a range of workload characteristics

SEAK Suite: The SEAK Suite is a collection of constraining problems for common embedded computing challenges. A constraining problem is a mission-centric and goal-oriented problem specification that separate problem-domain constraints from solution implementations so as to encourage creative solutions that meet goals but that may deviate from standard implementations.

PERFECT Suite: The PERFECT Suite consists of kernels and applications for evaluating tradeoffs between performance, power, and architecture within the domains of radar and image processing.

miniVite-x: Mini-application to demonstrate different memory patterns and test memory analysis tools.

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