Global Climate Justice Activism: “The New Protagonists” and their Projects for a Just Transition

The contributors to this volume have provided ample evidence to support calls for fundamental, transformative change in the world-system. If there remained any doubts, their analyses show that the capitalist world-system threatens not only the well-being of a majority of the world’s people, but also the very survival of our planet. Indeed, the urgency of the ecological and economic conditions that many people now face and the immense inequalities that have become more entrenched require that scholars become more consciously engaged in the work of advancing social transformation. Revolutionary change is emergent in movement spaces where people have long been working to develop shared analyses and cultivate collective power and agency by building unity among a diverse array of activists, organizations, and movements. We discuss three examples of transformative projects that are gaining increased visibility and attention: food sovereignty, solidarity economies, and Human Rights Communities. If widely adopted, these projects would undermine the basic processes necessary for the capitalist world-system to function. With these projects, defenders of environmental and social justice not only work to prevent their own (further) dispossession by denying capital its ability to continue appropriating labor and resources from working people and communities, but they also help deepen the existing systemic crisis while sowing the seeds of a new social order

GPU based techniques for deep image merging

Deep images store multiple fragments perpixel, each of which includes colour and depth, unlike traditional 2D flat images which store only a single colour value and possibly a depth value. Recently, deep images have found use in an increasing number of applications, including ones using transparency and compositing. A step in compositing deep images requires merging per-pixel fragment lists in depth order; little work has so far been presented on fast approaches. This paper explores GPU based merging of deep images using different memory layouts for fragment lists: linked lists, linearised arrays, and interleaved arrays. We also report performance improvements using techniques which leverage GPU memory hierarchy by processing blocks of fragment data using fast registers, following similar techniques used to improve performance of transparency rendering. We report results for compositing from two deep images or saving the resulting deep image before compositing, as well as for an iterated pairwise merge of multiple deep images. Our results show a 2 to 6 fold improvement by combining efficient memory layout with fast register based merging

Hybrid Lighting for faster rendering of scenes with many lights

There is growing interest in rendering scenes with many lights, where scenes typically contain hundreds to thousands of lights. Each light illuminates geometry within a finite extent called a light volume. A key aspect of performance is determining which lights apply to what geometry, and then applying those lights efficiently. We present a GPU-based approach using spatial data structures, binning lights by depth analytically while also taking advantage of hardware rasterization. This improves light binning performance by 3 We also present a GPU memory and cache friendly data structure that takes two passes to build, giving 410× improved performance when applying lighting and an overall improvement of 1.34× for total frametime