Linpack evaluation on a supercomputer with heterogeneous accelerators

Abstract—We report Linpack benchmark results on the TSUBAME supercomputer, a large scale heterogeneous system equipped with NVIDIA Tesla GPUs and ClearSpeed SIMD accelerators. With all of 10,480 Opteron cores, 640 Xeon cores, 648 ClearSpeed accelerators and 624 NVIDIA Tesla GPUs, we have achieved 87.01TFlops, which is the third record as a heterogeneous system in the world. This paper describes careful tuning and load balancing method required to achieve this performance. On the other hand, since the peak speed is 163 TFlops, the efficiency is 53%, which is lower than other systems. This paper also analyses this gap from the aspect of system architecture. I

Scalable Reed-Solomon-based Reliable Local Storage for HPC Applications on IaaS Clouds

International audienceWith increasing interest among mainstream users to run HPC applications, Infrastructure-as-a-Service (IaaS) cloud computing platforms represent a viable alternative to the acquisition and maintenance of expensive hardware, often out of the financial capabilities of such users. Also, one of the critical needs of HPC applications is an efficient, scalable and persistent storage. Unfortunately, storage options proposed by cloud providers are not standardized and typically use a different access model. In this context, the local disks on the compute nodes can be used to save large data sets such as the data generated by Checkpoint-Restart (CR). This local storage offers high throughput and scalability but it needs to be combined with persistency techniques, such as block replication or erasure codes. One of the main challenges that such techniques face is to minimize the overhead of performance and I/O resource utilization (i.e., storage space and bandwidth), while at the same time guaranteeing high reliability of the saved data. This paper introduces a novel persistency technique that leverages Reed-Solomon (RS) encoding to save data in a reliable fashion. Compared to traditional approaches that rely on block replication, we demonstrate about 50% higher throughput while reducing network bandwidth and storage utilization by a factor of 2 for the same targeted reliability level. This is achieved both by modeling and real life experimentation on hundreds of nodes

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.Comment: 12 pages, 10 figure

REMODEL: Rethinking Deep CNN Models to Detect and Count on a NeuroSynaptic System

In this work, we perform analysis of detection and counting of cars using a low-power IBM TrueNorth Neurosynaptic System. For our evaluation we looked at a publicly-available dataset that has overhead imagery of cars with context present in the image. The trained neural network for image analysis was deployed on the NS16e system using IBM's EEDN training framework. Through multiple experiments we identify the architectural bottlenecks present in TrueNorth system that does not let us deploy large neural network structures. Following these experiments we propose changes to CNN model to circumvent these architectural bottlenecks. The results of these evaluations have been compared with caffe-based implementations of standard neural networks that were deployed on a Titan-X GPU. Results showed that TrueNorth can detect cars from the dataset with 97.60% accuracy and can be used to accurately count the number of cars in the image with 69.04% accuracy. The car detection accuracy and car count (–/+ 2 error margin) accuracy are comparable to high-precision neural networks like AlexNet, GoogLeNet, and ResCeption, but show a manifold improvement in power consumption

腹膜および胸膜悪性中皮腫におけるEGFR発現の比較

An evaluation of epidermal growth factor receptor (EGFR) phenotypic expression in malignant pleural and peritoneal mesothelioma was undertaken, using immunohistochemical (IHC) and fluorescence in situ hybridization (FISH) analysis. Thirty-eight malignant mesothelioma (MM) specimens were subjected to IHC staining and FISH to evaluate the expression of EGFR protein and gene status. Overall positive IHC reaction was detected in 20/38 (53%) cases, in 11/22 (50%) pleural MM, and in 9/16 (56%) peritoneal MM. Our study confirmed that EGFR membranous expression is a common feature in MM, but not in benign mesothelial lesion. Thirty-seven cases did not show a gene copy number gain. Only one case showed a copy number gain. The protein overexpression of EGFR was not related to a gene copy number gain.博士（医学）・乙第1299号・平成24年5月28日© 2012 The Authors. Pathology International© 2012 Japanese Society of Pathology and Blackwell Publishing Asia Pty Ltd

Clinical response in Japanese metastatic melanoma patients treated with peptide cocktail-pulsed dendritic cells

BACKGROUND: Metastatic, chemotherapy-resistant melanoma is an intractable cancer with a very poor prognosis. As to immunotherapy targeting metastatic melanoma, HLA-A2(+ )patients were mainly enrolled in the study in Western countries. However, HLA-A24(+ )melanoma patients-oriented immunotherapy has not been fully investigated. In the present study, we investigated the effect of dendritic cell (DC)-based immunotherapy on metastatic melanoma patients with HLA-A2 or A24 genotype. METHODS: Nine cases of metastatic melanoma were enrolled into a phase I study of monocyte-derived dendritic cell (DC)-based immunotherapy. HLA-genotype analysis revealed 4 cases of HLA-A*0201, 1 of A*0206 and 4 of A*2402. Enriched monocytes were obtained using OptiPrep™ from leukapheresis products, and then incubated with GM-CSF and IL-4 in a closed serum-free system. After pulsing with a cocktail of 5 melanoma-associated synthetic peptides (gp100, tyrosinase, MAGE-2, MAGE-3 and MART-1 or MAGE-1) restricted to HLA-A2 or A24 and KLH, cells were cryopreserved until used. Finally, thawed DCs were washed and injected subcutaneously (s.c.) into the inguinal region in a dose-escalation manner. RESULTS: The mean percentage of DCs rated as lin(-)HLA-DR(+ )in melanoma patients was 46.4 ± 15.6 %. Most of DCs expressed high level of co-stimulatory molecules and type1 phenotype (CD11c(+)HLA-DR(+)), while a moderate number of mature DCs with CD83 and CCR7 positive were contained in DC products. DC injections were well tolerated except for transient liver dysfunction (elevation of transaminases, Grade I-II). All 6 evaluable cases except for early PD showed positive immunological responses to more than 2 melanoma peptides in an ELISPOT assay. Two representative responders demonstrated strong HLA-class I protein expression in the tumor and very high scores of ELISPOT that might correlate to the regression of metastatic tumors. Clinical response through DC injections was as follows : 1CR, 1 PR, 1SD and 6 PD. All 59 DC injections in the phase I study were tolerable in terms of safety, however, the maximal tolerable dose of DCs was not determined. CONCLUSIONS: These results suggested that peptide cocktail-treated DC-based immunotherapy had the potential for utilizing as one of therapeutic tools against metastatic melanoma in Japan