19 research outputs found

    GPU Concurrency: Weak Behaviours and Programming Assumptions

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    Concurrency is pervasive and perplexing, particularly on graphics processing units (GPUs). Current specifications of languages and hardware are inconclusive; thus programmers often rely on folklore assumptions when writing software. To remedy this state of affairs, we conducted a large empirical study of the concurrent behaviour of deployed GPUs. Armed with litmus tests (i.e. short concurrent programs), we questioned the assumptions in programming guides and vendor documentation about the guarantees provided by hardware. We developed a tool to generate thousands of litmus tests and run them under stressful workloads. We observed a litany of previously elusive weak behaviours, and exposed folklore beliefs about GPU programming---often supported by official tutorials---as false. As a way forward, we propose a model of Nvidia GPU hardware, which correctly models every behaviour witnessed in our experiments. The model is a variant of SPARC Relaxed Memory Order (RMO), structured following the GPU concurrency hierarchy

    Tyrosine kinase chromosomal translocations mediate distinct and overlapping gene regulation events

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    <p>Abstract</p> <p>Background</p> <p>Leukemia is a heterogeneous disease commonly associated with recurrent chromosomal translocations that involve tyrosine kinases including BCR-ABL, TEL-PDGFRB and TEL-JAK2. Most studies on the activated tyrosine kinases have focused on proximal signaling events, but little is known about gene transcription regulated by these fusions.</p> <p>Methods</p> <p>Oligonucleotide microarray was performed to compare mRNA changes attributable to BCR-ABL, TEL-PDGFRB and TEL-JAK2 after 1 week of activation of each fusion in Ba/F3 cell lines. Imatinib was used to control the activation of BCR-ABL and TEL-PDGFRB, and TEL-JAK2-mediated gene expression was examined 1 week after Ba/F3-TEL-JAK2 cells were switched to factor-independent conditions.</p> <p>Results</p> <p>Microarray analysis revealed between 800 to 2000 genes induced or suppressed by two-fold or greater by each tyrosine kinase, with a subset of these genes commonly induced or suppressed among the three fusions. Validation by Quantitative PCR confirmed that eight genes (Dok2, Mrvi1, Isg20, Id1, gp49b, Cxcl10, Scinderin, and collagen Vα1(Col5a1)) displayed an overlapping regulation among the three tested fusion proteins. Stat1 and Gbp1 were induced uniquely by TEL-PDGFRB.</p> <p>Conclusions</p> <p>Our results suggest that BCR-ABL, TEL-PDGFRB and TEL-JAK2 regulate distinct and overlapping gene transcription profiles. Many of the genes identified are known to be involved in processes associated with leukemogenesis, including cell migration, proliferation and differentiation. This study offers the basis for further work that could lead to an understanding of the specificity of diseases caused by these three chromosomal translocations.</p

    Correction: Molecular Subsets in the Gene Expression Signatures of Scleroderma Skin

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    Background: Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production. Methodology and Findings: We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of ‘intrinsic’ genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p , 0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud’s phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc. Conclusions and Significance: Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma
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