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Modern accelerators like GPUs increasingly execute independent operations concurrently to improve the device’s compute utilization. However, effectively harnessing it on GPUs for important primitives such as general matrix multiplications (GEMMs) remains challenging. Although modern GPUs have significant hardware and software GEMM support, their kernel implementations and optimizations typically assume each kernel executes inisolationand can utilize all GPU resources. This approach is highly efficient when kernels execute in isolation, but causes significant resource contention and slowdowns when kernels execute concurrently. Moreover, current approaches often onlystaticallyexpose and control parallelism within an application, without considering runtime information such as varying input size and concurrent applications – often exacerbating contention. These issues limit performance benefits from concurrently executing independent operations. Accordingly, we propose GOLDYLOC, which considers theglobalresources across all concurrent operations to identify performant GEMM kernels, which we call globally optimized (GO)-Kernels. GOLDYLOC also introduces a lightweight dynamic logic which considers thedynamicexecution environment for available parallelism and input sizes to execute performant combinations of concurrent GEMMs on the GPU. Overall, GOLDYLOC improves performance of concurrent GEMMs on a real GPU by up to 2 × (18% geomean per workload) versus the default concurrency approach and provides up to 2.5 × (43% geomean per workload) speedup over sequential execution. 

State-of-art secure processors like Intel SGX remain susceptible to leaking page-level address trace of an application via the page fault channel in which a malicious OS induces spurious page faults and deduces application's secrets from it. Prior works which fix this vulnerability do not provision for OS demand paging to be oblivious. In this work, we present InvisiPage which obfuscates page fault channel while simultaneously making OS demand paging oblivious. To do so, InvisiPage first carefully distributes page management actions between the application and the OS. Second, InvisiPage secures application's page management interactions with the OS using a novel construct which is derived from Oblivious RAM (ORAM) but is customized for page management. Finally, we lower overheads of our approach by reducing page management interactions with the OS via a novel memory partition. For a suite of cloud applications which process sensitive data we show that page fault channel can be tackled while enabling oblivious demand paging at low overheads.