147 research outputs found
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture
We introduce Stardust, a compiler that compiles sparse tensor algebra to
reconfigurable dataflow architectures (RDAs). Stardust introduces new
user-provided data representation and scheduling language constructs for
mapping to resource-constrained accelerated architectures. Stardust uses the
information provided by these constructs to determine on-chip memory placement
and to lower to the Capstan RDA through a parallel-patterns rewrite system that
targets the Spatial programming model. The Stardust compiler is implemented as
a new compilation path inside the TACO open-source system. Using cycle-accurate
simulation, we demonstrate that Stardust can generate more Capstan tensor
operations than its authors had implemented and that it results in 138
better performance than generated CPU kernels and 41 better performance
than generated GPU kernels.Comment: 15 pages, 13 figures, 6 tables
Inclusive Study Group Formation At Scale
Underrepresented students face many significant challenges in their
education. In particular, they often have a harder time than their peers from
majority groups in building long-term high-quality study groups. This challenge
is exacerbated in remote-learning scenarios, where students are unable to meet
face-to-face and must rely on pre-existing networks for social support.
We present a scalable system that removes structural obstacles faced by
underrepresented students and supports all students in building inclusive and
flexible study groups. One of our main goals is to make the traditionally
informal and unstructured process of finding study groups for homework more
equitable by providing a uniform but lightweight structure. We aim to provide
students from underrepresented groups an experience that is similar in quality
to that of students from majority groups. Our process is unique in that it
allows students the opportunity to request group reassignments during the
semester if they wish. Unlike other collaboration tools our system is not
mandatory and does not use peer-evaluation.
We trialed our approach in a 1000+ student introductory Engineering and
Computer Science course that was conducted entirely online during the COVID-19
pandemic. We find that students from underrepresented backgrounds were more
likely to ask for group-matching support compared to students from majority
groups. At the same time, underrepresented students that we matched into study
groups had group experiences that were comparable to students we matched from
majority groups. B-range students in high-comfort and high-quality groups had
improved learning outcomes
The Sparse Abstract Machine
We propose the Sparse Abstract Machine (SAM), an abstract machine model for
targeting sparse tensor algebra to reconfigurable and fixed-function spatial
dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse
primitives that encompass a large space of scheduled tensor algebra
expressions. SAM dataflow graphs naturally separate tensor formats from
algorithms and are expressive enough to incorporate arbitrary iteration
orderings and many hardware-specific optimizations. We also present Custard, a
compiler from a high-level language to SAM that demonstrates SAM's usefulness
as an intermediate representation. We automatically bind from SAM to a
streaming dataflow simulator. We evaluate the generality and extensibility of
SAM, explore the performance space of sparse tensor algebra optimizations using
SAM, and show SAM's ability to represent dataflow hardware.Comment: 18 pages, 17 figures, 3 table
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general
purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO
provides the flexibility needed to handle the requirements of modern autotuning
tasks. Particularly, it deals with permutation, ordered, and continuous
parameter types along with both known and unknown parameter constraints. To
reason about these parameter types and efficiently deliver high-quality code,
BaCO uses Bayesian optimiza tion algorithms specialized towards the autotuning
domain. We demonstrate BaCO's effectiveness on three modern compiler systems:
TACO, RISE & ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For
these domains, BaCO outperforms current state-of-the-art autotuners by
delivering on average 1.36x-1.56x faster code with a tiny search budget, and
BaCO is able to reach expert-level performance 2.9x-3.9x faster
A Cross-Sectional Study of Barriers to Personal Health Record Use among Patients Attending a Safety-Net Clinic
BACKGROUND: Personal health records (PHR) may improve patients' health by providing access to and context for health information. Among patients receiving care at a safety-net HIV/AIDS clinic, we examined the hypothesis that a mental health (MH) or substance use (SU) condition represents a barrier to engagement with web-based health information, as measured by consent to participate in a trial that provided access to personal (PHR) or general (non-PHR) health information portals and by completion of baseline study surveys posted there. METHODS: Participants were individually trained to access and navigate individualized online accounts and to complete study surveys. In response to need, during accrual months 4 to 12 we enhanced participant training to encourage survey completion with the help of staff. Using logistic regression models, we estimated odds ratios for study participation and for survey completion by combined MH/SU status, adjusted for levels of computer competency, on-study training, and demographics. RESULTS: Among 2,871 clinic patients, 70% had MH/SU conditions, with depression (38%) and methamphetamine use (17%) most commonly documented. Middle-aged patients and those with a MH/SU condition were over-represented among study participants (Nβ=β338). Survey completion was statistically independent of MH/SU status (OR, 1.85 [95% CI, 0.93-3.66]) but tended to be higher among those with MH/SU conditions. Completion rates were low among beginner computer users, regardless of training level (<50%), but adequate among advanced users (>70%). CONCLUSIONS: Among patients attending a safety-net clinic, MH/SU conditions were not barriers to engagement with web-based health information. Instead, level of computer competency was useful for identifying individuals requiring substantial computer training in order to fully participate in the study. Intensive on-study training was insufficient to enable beginner computer users to complete study surveys
Psoriasis Patients Are Enriched for Genetic Variants That Protect against HIV-1 Disease
An important paradigm in evolutionary genetics is that of a delicate balance between genetic variants that favorably boost host control of infection but which may unfavorably increase susceptibility to autoimmune disease. Here, we investigated whether patients with psoriasis, a common immune-mediated disease of the skin, are enriched for genetic variants that limit the ability of HIV-1 virus to replicate after infection. We analyzed the HLA class I and class II alleles of 1,727 Caucasian psoriasis cases and 3,581 controls and found that psoriasis patients are significantly more likely than controls to have gene variants that are protective against HIV-1 disease. This includes several HLA class I alleles associated with HIV-1 control; amino acid residues at HLA-B positions 67, 70, and 97 that mediate HIV-1 peptide binding; and the deletion polymorphism rs67384697 associated with high surface expression of HLA-C. We also found that the compound genotype KIR3DS1 plus HLA-B Bw4-80I, which respectively encode a natural killer cell activating receptor and its putative ligand, significantly increased psoriasis susceptibility. This compound genotype has also been associated with delay of progression to AIDS. Together, our results suggest that genetic variants that contribute to anti-viral immunity may predispose to the development of psoriasis
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